107 Integer Jobs

Patiënt Bezoeker Professional Zorgaanbod Campussen Het Jan Yperman Ziekenhuis is met meer dan 1400 medewerkers, 140 artsen en 532 bedden modern, jong en dynamisch. Een ziekenhuis waar de patiënt in het streven naar de beste zorg centraal staat en zeer veel aandacht aan kwalitatief hoogstaande geneeskunde wordt besteed. Als vernieuwend ziekenhuis investeren we in een stimulerende werkomgeving en teamwerk. We reiken volop kansen aan voor persoonlijke en professionele groei, met de focus op continue ontwikkeling en opleiding. In het Jan Yperman Ziekenhuis staan kinesitherapeuten klaar om patiënten te begeleiden bij uiteenlopende problemen aan het houdings- en bewegingsapparaat. Ze behandelen bijvoorbeeld mensen met spier-, gewrichts- of zenuwaandoeningen. Het Ziekenhuis Biedt Verschillende Gespecialiseerde Therapieën Aan, Waaronder Handrevalidatie: gericht op het herstel van hand- en polsproblemen. Manuele therapie: bij klachten van de wervelkolom en gewrichten. Oedeemtherapie: om vochtophopingen in het lichaam te verminderen. Kinderkinesitherapie: speciaal voor kinderen met motorische of ontwikkelingsproblemen. Psychosomatische kinesitherapie: voor klachten waarbij lichaam en geest samen een rol spelen, zoals chronische pijn of spanningsklachten. Daarnaast is er binnen de dienst Revalidatie speciale aandacht voor patiënten met een lichamelijke beperking of handicap. Door te revalideren, leren zij hoe ze zo zelfstandig mogelijk kunnen leven en omgaan met hun beperking. De dienst Fysische Geneeskunde, Revalidatie en Reumatologie bestaat uit een team van 6 artsen, 15 ergotherapeuten, 35 kinesitherapeuten, 4 logopedisten en 3 secretariaatsmedewerkers. Heb jij interesse om het te versterken? Functie-inhoud Als kinesitherapeut verzeker je aangepaste kinesitherapeutische zorgen aan de patiënten om hun capaciteiten en hun autonomie te behouden, te verbeteren en/of te herwinnen. Jouw Takenpakket Omvat Uitvoeren van onderzoeken en stellen van kinesitherapeutische diagnoses voor de patiënten, zoals het op basis van het medisch voorschrift voeren van de anamnese en de kinesitherapeutische onderzoeken om de vaardigheden te observeren, de stoornis te identificeren en de oorzaak van het probleem op te sporen; evalueren van de onderzoeksresultaten en een kinesitherapeutische diagnose stellen; bepalen van de optimale behandeling i.k.v. het multidisciplinair overleg; informeren over de onderzoeken, de vastgestelde stoornis en de aangeraden therapie; opstellen van een kinesitherapeutisch behandelingsplan. Behandelen van de patiënten en hun evolutie opvolgen, zoals volgens de door de artsen vooropgestelde therapie en aangepast aan hun profiel of specifieke pathologie; het motiveren van patiënten om actief mee te werken met zicht op een optimaal verloop van de therapie; regelmatig evalueren van de toegediende behandeling en eventuele aanpassingen in overleg met de patiënten en de betrokken zorgverleners doen; adviseren en informeren van, indien nodig, de andere betrokkenen (bv. de familie) over de evolutie van de patiënten; meewerken aan het optimaliseren van therapieën op basis van de evoluties binnen het vak domein. Administratief opvolgen van de uitgevoerde behandelingen, zoals het organiseren van de eigen werkdag volgens de kinesitherapeutische interventievragen en conform de regelgeving van kracht; actueel houden van het kinesitherapeutisch deel in het patiëntendossier, opstellen van een rapport over elke uitgevoerde behandeling en de evolutie van de patiënten; invullen van de vereiste administratieve documenten voor de facturatie en opname van kinesitherapeutische zorgen door de verzekeringsinstellingen (bv. mutualiteiten, verzekeringen arbeidsongevallen). Deelnemen aan interdisciplinaire, preventie- of educatieve activiteiten, zoals aan (inter)disciplinaire vergaderingen over de algemene opvolging van patiënten en het zorgbeleid (bespreken van casussen, intervisie enz.) en preventiewerkgroepen (bv. rugschool) of thematische werkgroepen (bv. nieuwe technieken en automatisering in de kinesitherapeutische zorgen); animeren en/of organiseren van collectieve activiteiten voor de continuïteit van de kinesitherapeutische zorgen (bv. gymles). Deelnemen aan overlegvergaderingen bij de aankoop van gespecialiseerd materiaal. Raadplegingen uitvoeren bij ambulante patiënten. Functieprofiel Je bent master in de Revalidatiewetenschappen en Kinesitherapie. Je combineert je gedegen algemene beroepskennis met affiniteit voor de betreffende patiëntenpopulatie. Je beschikt over een grote portie aan integriteit en inlevingsvermogen. Je kan duidelijk en objectief in groep communiceren en het eigen standpunt verdedigen. Je kan met de specifieke informaticatoepassingen werken. Kennis van Word, Excel en KWS is een meerwaarde of je bent hierin een vlotte leerling. Je bent open, respectvol en integer en klantvriendelijkheid zit in je DNA. Je kan georganiseerd en accuraat handelen en daarbij je zaken terdege afwerken. Je fungeert als voorbeeld voor kwaliteitsgerichtheid en professionaliteit. Je zoekt actief naar mogelijkheden voor het vergroten van de eigen deskundigheid en verdere professionele en persoonlijke ontwikkeling. Wij bieden je Een contract op zelfstandige basis voor 9 maanden met de duidelijke intentie om dit nadien om te zetten naar onbepaalde duur. Of opname in de werfreserve. Een deel- tot voltijds werkregime. Een wisselend uurrooster met shiften tussen 8u en 20u. 1 zaterdag op 4 en 1 volledig weekend op 12 werken. Een uitstekende verloning. On-the-job inwerking. Tal van voordelen zoals hospitalisatieverzekering (via polis zelfstandigen), gratis griepvaccin, kortingen via Benefits@work, Bringme box en een betaalbaar personeelsrestaurant. Contracttype Zelfstandig Solliciteren kan tot 17/08/2025 Meer Info Via Sylvie Witdouck, dienstverantwoordelijke-hoofd Kinesitherapie: 057 35 73 86. Sofie Verschoore, verpleegkundige-diensthoofd: 057 35 70 58. Dr. Geert Moyaert, revalidatiearts: 057 35 73 62. Veel succes! Solliciteer hier online Jan Yperman Ziekenhuis Briekestraat 12 8900 Ieper 057 35 35 35 info@yperman.net Ondernemingsnummer: BE 0462.915.078 Rechtspersonenregister (RPR) Gent, afdeling Ieper Alle campussen

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. EY GDS – Data and Analytics (D&A) – Senior – Senior Data Scientist Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 3 - 7 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Your technical responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI. Collaborate with stakeholders to identify business opportunities and define AI project goals. Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus. Minimum 3-7 years of experience in Data Science and Machine Learning. In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills: Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems. Utilize optimization tools and techniques, including MIP (Mixed Integer Programming). Drive DevOps and MLOps practices, covering continuous integration, deployment, and monitoring of AI models. Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. EY GDS – Data and Analytics (D&A) – Senior – Senior Data Scientist Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 3 - 7 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Your technical responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI. Collaborate with stakeholders to identify business opportunities and define AI project goals. Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus. Minimum 3-7 years of experience in Data Science and Machine Learning. In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills: Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems. Utilize optimization tools and techniques, including MIP (Mixed Integer Programming). Drive DevOps and MLOps practices, covering continuous integration, deployment, and monitoring of AI models. Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. EY GDS – Data and Analytics (D&A) – Senior – Senior Data Scientist Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 3 - 7 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Your technical responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI. Collaborate with stakeholders to identify business opportunities and define AI project goals. Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus. Minimum 3-7 years of experience in Data Science and Machine Learning. In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills: Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems. Utilize optimization tools and techniques, including MIP (Mixed Integer Programming). Drive DevOps and MLOps practices, covering continuous integration, deployment, and monitoring of AI models. Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. EY GDS – Data and Analytics (D&A) – Senior – Senior Data Scientist Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 3 - 7 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Your technical responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI. Collaborate with stakeholders to identify business opportunities and define AI project goals. Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus. Minimum 3-7 years of experience in Data Science and Machine Learning. In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills: Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems. Utilize optimization tools and techniques, including MIP (Mixed Integer Programming). Drive DevOps and MLOps practices, covering continuous integration, deployment, and monitoring of AI models. Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. EY GDS – Data and Analytics (D&A) – Senior – Senior Data Scientist Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 3 - 7 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Your technical responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI. Collaborate with stakeholders to identify business opportunities and define AI project goals. Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus. Minimum 3-7 years of experience in Data Science and Machine Learning. In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills: Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems. Utilize optimization tools and techniques, including MIP (Mixed Integer Programming). Drive DevOps and MLOps practices, covering continuous integration, deployment, and monitoring of AI models. Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

Job Title: Supply Chain Optimization Specialist Experience: 3+ Years Department: Operations Research / Supply Chain Analytics Position Overview: We are seeking a highly analytical and skilled Supply Chain Optimization Specialist with a strong background in mathematical modeling, optimization, and data analysis. The ideal candidate will play a critical role in improving supply chain operations by developing advanced models and providing data-driven insights. You will collaborate with cross-functional teams to ensure effective implementation of optimized solutions in real-world supply chain systems. Key Responsibilities: Mathematical Modeling & Optimization Develop, refine, and validate mathematical models for inventory management, production planning, transportation logistics, and distribution networks. Apply advanced optimization techniques including linear programming, integer programming, network flows, simulation, and heuristics to solve complex supply chain challenges. Perform sensitivity analysis, scenario modeling, and risk assessment to evaluate system performance under various conditions. Translate business objectives, constraints, and requirements into mathematical frameworks and optimization problems. Data Analysis & Insights Analyze large-scale supply chain data to extract actionable insights and identify performance trends. Partner with data scientists and analysts to gather, clean, and preprocess data from multiple sources ensuring accuracy and completeness. Provide recommendations to optimize cost, improve efficiency, and enhance customer satisfaction through data-driven decisions. Solution Development & Deployment Present analytical findings, models, and recommendations to stakeholders in a clear, structured format. Provide input on trade-offs between analytical rigor and speed-to-market solutions. Collaborate with internal teams including Data Engineers, Data Scientists, Business Analysts, and Project Managers to test and deploy solutions effectively. Research & Innovation Stay abreast of emerging trends in supply chain management, operations research, and optimization methodologies. Research and propose innovative approaches to address new and evolving supply chain challenges. Qualifications: Master’s degree in Industrial Engineering, Operations Research, Management Science , or a related field. 3+ years of professional experience in supply chain modeling and optimization. Strong command of optimization techniques such as linear/integer programming, network flow modeling, simulation, and heuristic algorithms . Programming proficiency in Python, R , or MATLAB , with hands-on experience using optimization libraries like Gurobi, CPLEX, FICO . Expertise in data manipulation using pandas, NumPy , and similar tools. Solid understanding of SQL for data extraction; experience with visualization platforms like Tableau or Power BI . Strong knowledge of supply chain processes, including demand forecasting, inventory management, production planning, transportation logistics , and distribution networks . Preferred Skills: Excellent problem-solving and critical thinking abilities. Strong communication skills to explain technical solutions to non-technical stakeholders. Experience working in cross-functional and collaborative environments.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. Job Description: EY GDS – Data and Analytics - D and A – Senior – Senior Data Scientist Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 3 - 7 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Your technical responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI. Collaborate with stakeholders to identify business opportunities and define AI project goals. Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements : Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus. Minimum 3-7 years of experience in Data Science and Machine Learning. In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills: Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems. Utilize optimization tools and techniques, including MIP (Mixed Integer Programming). Drive DevOps and MLOps practices, covering continuous integration, deployment, and monitoring of AI models. Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

Location: UK / Europe (Remote or Hybrid) Looking for a highly analytical and technically skilled Optimization Specialist to build and scale mathematical models and process optimization. Candidates must show strong grounding in operations research, simulation, and/or machine learning. Design and implement optimization models (e.g., LP, MIP, MILP) for real-world scheduling, routing, and planning scenarios. Build simulation models to evaluate performance under uncertainty (e.g., disruptions, variable demand). Collaborate with data scientists, software engineers, and business analysts to refine problem definitions and translate them into quantitative models. Use AI/ML algorithms (forecasting, clustering, classification) to drive predictive optimization workflows. Conduct what-if analyses and sensitivity testing to support decision-making. Support research and pilot initiatives involving Quantum-Inspired Optimization (QUBO, hybrid models). Present findings and model performance to stakeholders with clear, concise visualizations and documentation. Strong knowledge of Linear Programming (LP), Mixed Integer Programming (MIP/MILP), Constraint Programming. Formulating and solving large-scale combinatorial problems Hands-on experience with Optimization libraries like Pyomo, PuLP, Google OR-Tools Solvers like CPLEX, Gurobi, GLPK, or CBC Programming proficiency in Python, R, or MATLAB Simulation expertise in SimPy, AnyLogic, Arena, or equivalent tools for discrete-event or agent-based simulation Applied machine learning knowledge Forecasting models, clustering, and model evaluation metrics (e.g., MAPE, RMSE) Clear communication and documentation skills to present models and insights to non-technical audiences Educational background PhD in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or related field

Data Scientist Hyderabad Exp: 4+ Years Key Responsibilities: Design, develop, and deploy Forecasting and predictive ML models in Azure Environment using pyspark. Develop and maintain ML models using Data Bricks integrate them with other Azure services. Handle end-to-end model lifecycle, including versioning control, testing, validation, and deployment in production environments (Azure Cloud). Establish and manage secure database connections and perform data extraction and manipulation using SQL. Monitor, maintain, and continuously improve deployed models for performance and accuracy. Must-Have Skills: Minimum 4+ years of experience in data science, machine learning with strong python backend. Strong hands-on experience with Microsoft Azure, with and other related services. Proficiency in Python and commonly used libraries (e.g., Pandas, NumPy, Scikit[1]learn etc.). Expertise in SQL and experience in working with relational and non-relational databases. Proven experience in developing and deploying optimization models (e.g., linear programming, mixed-integer programming). Strong understanding of model deployment pipelines and integration with business applications. Familiarity with version control (e.g., Git) and CI/CD practices.

Company Profile : That's My Craft interior designing studio operating since 2016. We specialised in providing comprehensive turnkey solutions for a wide range of integer design projects. With a close-knit team we strictly work with fresh philosophy every time. Responsibilities : The key responsibilities area (KRA) of 2D Interior Designer :- 1. Drawing & Drafting - Develop 2D floor plans, furniture space planning , Furniture elevations, sections, and detailed working drawings (Civil work, electrical, plumbing, Reflected celling plan (RCP), etc.) - Convert site measurements, concept sketches, and verbal inputs into clear technical drawings - Prepare detailed drawings for custom furniture, cabinetry, and interior elements - Maintain a drawing library and template consistency across projects 2. Collaboration Coordinate with the design and execution team to ensure drawing accuracy and feasibility 3. Technical Accuracy Ensure that all drawings follow industry drafting standards, including proper dimensioning, layering, and annotations 4. Software & File Management - Work daily in AutoCAD, and optionally SketchUp/Layout for presentation-style 2D exports Required Skills & Qualifications - Proficiency in AutoCAD (mandatory) - Knowledge of basic SketchUp is a plus - Good understanding of interior materials, detailing, and joinery principles - Ability to interpret conceptual inputs into executable technical outputs - Familiarity with building codes, electrical, plumbing, and civil coordination basics is preferred Soft Skills - Strong attention to detail and accuracy - Ability to work under pressure and meet deadlines - Proactive communicator – ability to clarify inputs and flag drawing conflicts early - A team player with a collaborative mindset Preferred Background - Diploma/Degree in Interior Design, Architecture, or related field - 1 years of experience in 2D drafting roles within architecture or interior studios - Portfolio demonstrating technical drawing capabilities and design sensibility How to Apply: Send your resume & portfolio( showcasing relevant 3D visualization work, including examples of interior renderings and video walkthroughs) at hr@thatsmycraft.com or contact on this number +91 9023333832. Job Types: Full-time, Permanent, Fresher Pay: ₹10,000.00 - ₹15,000.00 per month Benefits: Internet reimbursement Leave encashment Paid time off Ability to commute/relocate: Gandhinagar, Gujarat: Reliably commute or planning to relocate before starting work (Required) Education: Diploma (Required) Experience: Interior design: 1 year (Required) Language: Hindi , Gujarati , English (Required) Work Location: In person

Company Profile : That's My Craft interior designing studio operating since 2016. We specialised in providing comprehensive turnkey solutions for a wide range of integer design projects. With a close-knit team we strictly work with fresh philosophy every time. Responsibilities : The key responsibilities area (KRA) of interior designer :- 1. 3D Modelling & visualisation : Assisting in creating 3D models and visualisation to help team better understand design concept. 2. Cad Drawing : Creating detailed CAD drawing and Technical documentation for design plans 3. Space Planning : Assisting on space layout, furniture arrangements and ensuring optimal space utilisation. 4. Project Documentation : Maintaining project files, documentation and ensuring designs specifications are accurately recorded. 5. Contribution to design discussions : Problem solving and understanding how to balance aesthetics and functionality. 6. Learning and Growth : Gains skills related to software, designing process, execution process with the help of senior designer and mentor. 7. Collaboration and Teamwork : Actively participating in team meeting sessions and contributing to the overall success of the design project. 8. Time Management : Complete assigned task and projects within define timeline provided by Senior designer. How to Apply: Send your resume & portfolio( showcasing relevant 3D visualization work, including examples of interior renderings and video walkthroughs) at hr@thatsmycraft.com or contact on this number +91 9023333832. Job Types: Full-time, Permanent, Fresher Pay: ₹25,000.00 - ₹45,000.00 per month Benefits: Internet reimbursement Leave encashment Paid time off Ability to commute/relocate: Gandhinagar, Gujarat: Reliably commute or planning to relocate before starting work (Required) Education: Diploma (Required) Experience: Interior design: 1 year (Required) Language: Hindi, Gujarati , English (Required) Work Location: In person

University: Delft University of Technology Country: Netherlands Deadline: 2025-08-31 Fields: Computer Science, Operations Research, Artificial Intelligence, Applied Mathematics, Statistics Are you passionate about harnessing the power of artificial intelligence to transform decision-making under uncertainty? Do you aspire to develop innovative algorithms that can offer robust and reliable recommendations for critical real-world problems? If your academic ambitions lie at the intersection of symbolic AI, optimisation, and statistical reasoning, this PhD position at TU Delft could be your gateway to cutting-edge research and impactful career opportunities. Strong and confident decision-making is at the core of many societal and engineering challenges today. Yet, the complexity and uncertainty inherent in real-world systems often make it difficult to ascertain which variables truly influence outcomes. This PhD project invites you to leverage advanced combinatorial and algorithmic methods to identify these critical parameters, thus enabling more robust, data-efficient, and reliable decision-making processes. About The University Or Research Institute Delft University of Technology (TU Delft) is one of Europe’s premier technical universities, renowned for its pioneering research, high-impact innovation, and vibrant academic community. Located in the historic city of Delft, the university boasts a strong tradition in engineering, science, and design, with a global reputation for excellence. TU Delft has been at the forefront of groundbreaking advancements, from the world-famous Dutch waterworks to state-of-the-art biotech and digital technologies. As a PhD candidate at TU Delft, you will join a diverse and international research environment, collaborating with leading experts and contributing to solutions for some of society’s most pressing challenges. Research Topic and Significance The focus of this PhD position is on Symbolic AI and Reasoning Under Uncertainty, with a particular emphasis on sensitivity analysis for decision-making models. In contemporary applications, algorithms generate recommendations based on complex models with numerous input parameters. However, determining which of these inputs genuinely drive the outcomes is crucial for resource allocation, contingency planning, and ensuring the robustness of decisions. Also See Postdoctoral Opportunity in Harmonic Mitigation for Power Electronics-Based Power Systems at… Postdoctoral Opportunity in Applied Planning and Scheduling Under Uncertainty for Offshore… Postdoctoral Opportunity in Harmonic Mitigation for Power Electronics-Based Power Systems at… PhD Position in ML-Accelerated Simulations and Uncertainty Quantification of Sustainable… PhD Opportunities in Agentic/LLM Reasoning at IDIAP/EPFL Traditional sensitivity analysis often relies on computationally intensive simulations, which may overlook significant variables. This project proposes a paradigm shift: employing logic-based and structural methods to formally identify critical parameters. By exploiting the structure of logic problems, the research aims to deliver formal guarantees on robustness, reduce computational costs, and improve the reliability of decision-support systems. This approach holds immense promise for fields such as engineering, policy-making, and societal infrastructure, where robust and explainable AI is essential. Project Details As a PhD candidate, you will be part of the Algorithmics section within the Software Technology (ST) department at TU Delft, collaborating closely with the Statistics section of the Delft Institute of Applied Mathematics (DIAM). The project is supervised by a distinguished team: dr. ir. Sicco Verwer (promotor), dr. Fabian Mies, and dr. Anna Latour (co-promotores). Your research will build upon and expand knowledge in modelling and solving paradigms such as Boolean Satisfiability, Constraint Programming, and Mixed-Integer (Linear) Programming. You will also develop methods for reasoning about discrete probability distributions, sampling complex spaces, and statistical analysis. The role involves collaboration with other researchers and software engineers to create practical tools for both academic and societal stakeholders. You will disseminate your findings through publications and presentations at leading conferences and journals, providing excellent opportunities for international networking and professional growth. The Algorithmics section is a dynamic, diverse environment where PhD and postdoc researchers work together on theoretical and algorithmic contributions to intelligent decision-making. The group addresses challenges including scalability, model learning for planning and verification, and the integration of stakeholder preferences into algorithmic processes. TU Delft’s interdisciplinary ecosystem further enhances your research experience, with opportunities to engage in cross-faculty initiatives addressing topics such as climate change, energy transition, and artificial intelligence. Candidate Profile Applicants Should Possess The Following Qualifications And Attributes – A Master’s degree in Computer Science, Operations Research, or a related field (required by the Graduate School of TU Delft). – Proficiency in the English language (see https://www.tudelft.nl/onderwijs/opleidingen/phd/admission). – Demonstrable knowledge of reasoning paradigms such as Boolean Satisfiability, Constraint Programming, or Mixed Integer (Linear) Programming (preferred). – Programming skills in languages such as Java, Python, or C++ (preferred). – Solid understanding of statistics and probability theory, particularly hypothesis testing. – Excellent critical and analytical thinking skills. – A proven record of, and interest in, further developing research skills, including self-organisation, academic writing, and a critical attitude. – Strong enthusiasm for state-of-the-art algorithmic and optimisation techniques, especially their application to decision-making. – Affinity for teaching and mentoring students. – Ability to work effectively in a team and take initiative. This position is ideal for candidates who are passionate about symbolic AI, optimisation, statistical reasoning, and who are eager to contribute to the advancement of robust decision-making methodologies. Application Process Are You Interested In This Vacancy? Please Apply No Later Than 31 Aug 2025 Via The Application Button And Upload The Following Documents – CV – Motivational letter You can address your application to Dr. Anna Latour. In addition to the required documents (your tabular CV and cover letter), we ask you to do the following: – Submit a recent example of your own academic writing (e.g., a draft of your MSc thesis). – Select 1-3 publications by Dr. Anna Latour. For each publication, please write a short paragraph addressing: (1) What you found most interesting or surprising about the research and why, and (2) What follow-up research you would propose based on your reading and why. Please submit these paragraphs along with your application. Apply via the application button and upload your tabular CV, cover letter, sample of recent academic writing, and paragraph(s) about Dr. Latour’s publication(s). Apply: https://careers.tudelft.nl/job/Delft-PhD-Position-Symbolic-AI-and-Reasoning-Under-Uncertainty-2628-CD/824585702/ Conclusion This PhD position at TU Delft offers a unique opportunity to engage in pioneering research at the intersection of symbolic AI, optimisation, and statistical reasoning. If you are driven to make a meaningful impact on decision-making methodologies and thrive in a collaborative, interdisciplinary environment, we encourage you to apply. Stay tuned to similar opportunities by following updates below this post. Want to calculate your PhD admission chances? Try it here: https://phdfinder.com/phd_admission_chance_calculator/ Get the latest openings in your field and preferred country—straight to your email inbox. 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We're Celonis, the global leader in Process Mining technology and one of the world's fastest-growing SaaS firms. We believe there is a massive opportunity to unlock productivity by placing data and intelligence at the core of business processes - and for that, we need you to join us. The Team: The Celonis Supply Chain team is a young team of product enthusiasts that focuses on developing best-in class Business Apps in the domain of Supply Chain. Business Apps are purpose-built solutions created for a specific line of business, addressing most impactful pain points in their respective domain. The apps are best practice examples of how Celonis is used in new, innovative ways that push the boundaries of our platform. The team has a great balance of technical skills to build solutions paired with a customer focus and product management mindset to create meaningful products widely adopted across our customer base. The Role: You will gain experience in the Supply Chain and related processes and act as the interface between customers, our Go-To-Market and our Product & Engineering teams, shaping the future of Celonis’ Business Apps in this domain. Take this chance to rethink the way companies run their Supply Chain processes and help our customers to accelerate their value realization with Celonis. The work you’ll do: Work closely with customers to understand their pain points in Supply Chain with a focus on Order Management Be involved in all steps within the product development lifecycle from ideation to product development to scaling solutions across all Celonis customers Use the Celonis Process Intelligence Platform hands-on to develop new, innovative solutions in the area of Order Management Collaborate with platform product managers and engineers to guide and influence feature development of the platform Work together with our UX team to define the user experience for our products If you are passionate about Supply Chain and Order Management, the Celonis technology, and what it can do for our customers, here's your chance to directly contribute to taking our Celonis Process Intelligence Platform to the next level. The qualifications you need: 6-9+ years of experience in Solution Engineering, Implementation Consulting, Business Analytics, Operations Research or comparable roles Expert in data analysis including with tools native to the Celonis platform: SQL, PQL and Python Experience with SAP, Oracle or other ERP systems and their respective Order Management modules Operations research experience including (Mixed Integer) Linear Programming and inventory optimization is a plus Experience working with MRP is a plus Project management experience and excellent planning and organizational skills Creativity in problem solving, solution-oriented, self-motivated, able to work independently and collaborate well within and outside the team Excellent verbal and written communication skills and the willingness to work very closely with customers and cross-functional stakeholders Very good knowledge of spoken and written English. What Celonis Can Offer You: Pioneer Innovation: Work with the leading, award-winning process mining technology, shaping the future of business. Accelerate Your Growth: Benefit from clear career paths, internal mobility, a dedicated learning program, and mentorship opportunities. Receive Exceptional Benefits: Including generous PTO, hybrid working options, company equity (RSUs), comprehensive benefits, extensive parental leave, dedicated volunteer days, and much more. Interns and working students explore your benefits here. Prioritize Your Well-being: Access to resources such as gym subsidies, counseling, and well-being programs. Connect and Belong: Find community and support through dedicated inclusion and belonging programs. Make Meaningful Impact: Be part of a company driven by strong values that guide everything we do: Live for Customer Value, The Best Team Wins, We Own It, and Earth Is Our Future. Collaborate Globally: Join a dynamic, international team of talented individuals. Empowered Environment: Contribute your ideas in an open culture with autonomous teams. About Us: Celonis makes processes work for people, companies and the planet. The Celonis Process Intelligence Platform uses industry-leading process mining and AI technology and augments it with business context to give customers a living digital twin of their business operation. It’s system-agnostic and without bias, and provides everyone with a common language for understanding and improving businesses. Celonis enables its customers to continuously realize significant value across the top, bottom, and green line. Celonis is headquartered in Munich, Germany, and New York City, USA, with more than 20 offices worldwide. Get familiar with the Celonis Process Intelligence Platform by watching this video. Celonis Inclusion Statement: At Celonis, we believe our people make us who we are and that “The Best Team Wins”. We know that the best teams are made up of people who bring different perspectives to the table. And when everyone feels included, able to speak up and knows their voice is heard - that's when creativity and innovation happen. Your Privacy: Any information you submit to Celonis as part of your application will be processed in accordance with Celonis’ Accessibility and Candidate Notices By submitting this application, you confirm that you agree to the storing and processing of your personal data by Celonis as described in our Privacy Notice for the Application and Hiring Process. Please be aware of common job offer scams, impersonators and frauds. Learn more here.

The role entails advanced software development for Power Systems Applications, with a focus on delivering specific functionalities to meet corporate project and product objectives. Responsibilities include collaborating with team working with Electric Utilities or Independent System Operators (ISOs) and Transmission and Distribution System Operators to develop functional software specifications, followed by designing, coding, testing, integration, application tuning, and delivery Job Description Roles and Responsibilities As a senior member of the Software Center of Excellence, exemplifying high-quality development, testing, and delivery practices Responsible for enhancing, evolving, and supporting high-availability Electricity Energy Market Management System (MMS) Responsible for development, testing, integration, and tuning of advanced Power Systems Application software to fulfill project and product commitments Develop and evolve software in a dynamic and agile environment using the latest technologies and infrastructure Provide domain knowledge and/or technical leadership to a team of electricity markets application software engineers Support in providing budget estimates for new project tasks to project leads and managers Collaborate with customers throughout the project lifecycle to ensure software quality and functionality meet standards and requirements Interact with Product Development Teams, Customers, Solution Providers, and cross-functional teams as needed Apply SDLC principles and methodologies like Lean/Agile/XP, CI, software and product security, scalability, and testing techniques Provide maintenance of power systems application functionality, including code fixes, creating tools for model conversion, documentation, and user interfaces Support marketing efforts for proposals and demonstrations to potential customers Basic Qualification Master's degree in Electrical Power Systems with thesis or related work in power systems 5 to 8 years of experience in development or project delivery, preferably in Power Systems Analysis, Security Constrained Unit Commitment and Economic Dispatch using Mixed Integer Programming (MIP)/Optimization, or Applied Mathematics and Operations Research Desired Characteristics Continuous improvement mindset; drives change initiatives and process improvements Highly organized and efficient; adept at prioritizing and executing tasks Experience in the power systems domain Proficiency in testing and test automation Strong knowledge of source control management, particularly GitHub Demonstrated ability to learn new development practices, languages, and tools Self-motivated; able to synthesize information from diverse sources Continuously measures the completion rate of personal deliverables and compares them to the scheduled commitments Transparent in problem-solving approaches and options; determines fair outcomes with shared trade-offs Capable of defining requirements and collaborating on solutions using technical expertise and a network of experts Effective communication style for engaging with customers and cross-functional teams; utilizes product knowledge to mitigate risks and drive outcomes Strong verbal, written, and interpersonal communication skills; able to produce professional and technical reports and conduct presentations Innovates and integrates new processes or technologies to add significant value; advises on change cost versus benefits and learns new solutions to address complex problems

The role entails advanced software development for Power Systems Applications, with a focus on delivering specific functionalities to meet corporate project and product objectives. Responsibilities include collaborating with team working with Electric Utilities or Independent System Operators (ISOs) and Transmission and Distribution System Operators to develop functional software specifications, followed by designing, coding, testing, integration, application tuning, and delivery. Job Description Roles and Responsibilities As a senior member of the Software Center of Excellence, exemplifying high-quality design, development, testing, and delivery practices Responsible for enhancing, evolving, and supporting high-availability Electricity Energy Market Management System (MMS) Lead the design, development, testing, integration, and tuning of advanced Power Systems Application software to fulfill project and product commitments Develop and evolve software in a dynamic and agile environment using the latest technologies and infrastructure Provide domain knowledge and/or technical leadership to a team of electricity markets application software engineers Provide budget estimates for new project tasks to project leads and managers Collaborate with customers throughout the project lifecycle to ensure software quality and functionality meet standards and requirements Mentor junior team members Interact with Product Development Teams, Customers, Solution Providers, and cross-functional teams as needed Apply SDLC principles and methodologies like Lean/Agile/XP, CI, software and product security, scalability, and testing techniques Provide maintenance of power systems application functionality, including code fixes, creating tools for model conversion, documentation, and user interfaces Support marketing efforts for proposals and demonstrations to potential customers Basic Qualification Ph. D. or Master's degree in Electrical Power Systems with thesis or related work in power systems 8 to 11 years of experience in development or project delivery, preferably in Power Systems Analysis, Security Constrained Unit Commitment and Economic Dispatch using Mixed Integer Programming (MIP)/Optimization, or Applied Mathematics and Operations Research Desired Characteristics Continuous improvement mindset; drives change initiatives and process improvements Highly organized and efficient; adept at prioritizing and executing tasks Experience in the power systems domain Proficiency in testing and test automation Strong knowledge of source control management, particularly GitHub Demonstrated ability to learn new development practices, languages, and tools Self-motivated; able to synthesize information from diverse sources Mentors newer team members in alignment with business objectives Continuously measures the completion rate of personal deliverables and compares them to the scheduled commitments Transparent in problem-solving approaches and options; determines fair outcomes with shared trade-offs Capable of defining requirements and collaborating on solutions using technical expertise and a network of experts Effective communication style for engaging with customers and cross-functional teams; utilizes product knowledge to mitigate risks and drive outcomes Strong verbal, written, and interpersonal communication skills; able to produce professional and technical reports and conduct presentations Innovates and integrates new processes or technologies to add significant value; advises on change cost versus benefits and learns new solutions to address complex problems

The role entails advanced software development for Power Systems Applications, with a focus on delivering specific functionalities to meet corporate project and product objectives. Responsibilities include collaborating with team working with Electric Utilities or Independent System Operators (ISOs) and Transmission and Distribution System Operators to develop functional software specifications, followed by designing, coding, testing, integration, application tuning, and delivery. Job Description Roles and Responsibilities As a team member of the Software Center of Excellence, exemplifying high-quality development, testing, and delivery practices Responsible for enhancing, evolving, and supporting high-availability Electricity Energy Market Management System (MMS) Responsible for development, testing, integration, and tuning of advanced Power Systems Application software to fulfill project and product commitments Develop and evolve software in a dynamic and agile environment using the latest technologies and infrastructure Provide domain knowledge and/or technical support to a team of electricity markets application software engineers Understand customers' needs and focus throughout the project lifecycle to ensure software quality and functionality meet standards and requirements Interact with Product Development Teams, Customers, Solution Providers, and cross-functional teams as needed Apply SDLC principles and methodologies like Lean/Agile/XP, CI, software and product security, scalability, and testing techniques Provide maintenance of power systems application functionality, including code fixes, creating tools for model conversion, documentation, and user interfaces Basic Qualification Master's degree in Electrical Power Systems with thesis or related work in power systems 3 to 5 years of experience in development or project delivery, preferably in Power Systems Analysis, Security Constrained Unit Commitment and Economic Dispatch using Mixed Integer Programming (MIP)/Optimization, or Applied Mathematics and Operations Research Desired Characteristics Continuous improvement mindset; drives change initiatives and process improvements Highly organized and efficient; adept at prioritizing and executing tasks Experience in the power systems domain Proficiency in testing and test automation Experience in programming skills such as C++ or Java or other related language, as required. Knowledge on Perl, PowerShell and SQL (MSSQL and/or T-SQL) scripting languages. Good understanding of database operation Strong knowledge of source control management, particularly GitHub Demonstrated ability to learn new development practices, languages, and tools Self-motivated; able to synthesize information from diverse sources Continuously measures the completion rate of personal deliverables and compares them to the scheduled commitments Transparent in problem-solving approaches and options; determines fair outcomes with shared trade-offs Capable of defining requirements and collaborating on solutions using technical expertise and a network of experts Effective communication style for engaging with customers and cross-functional teams; utilizes product knowledge to mitigate risks and drive outcomes Strong verbal, written, and interpersonal communication skills; able to produce professional and technical reports and conduct presentations Innovates and integrates new processes or technologies to add significant value; advises on change cost versus benefits and learns new solutions to address complex problems

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. Job Description: Senior Data Scientist Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 4 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Your technical responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI. Collaborate with stakeholders to identify business opportunities and define AI project goals. Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, Agentic Framework to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus. Minimum 4 years of experience in Data Science and Machine Learning. In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills: Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models Utilize optimization tools and techniques, including MIP (Mixed Integer Programming. Deep knowledge of classical AIML (regression, classification, time series, clustering) Drive DevOps and MLOps practices, covering CI/CD and monitoring of AI models. Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

Job Summary: We are seeking a proactive and detail-oriented Data Scientist to join our team and contribute to the development of intelligent AI-driven production scheduling solutions. This role is ideal for candidates passionate about applying machine learning, optimization techniques, and operational data analysis to enhance decision-making and drive efficiency in manufacturing or process industries. You will play a key role in designing, developing, and deploying smart scheduling algorithms integrated with real-world constraints like machine availability, workforce planning, shift cycles, material flow, and due dates. Experience: 1 Year Responsibilities: 1. AI-Based Scheduling Algorithm Development Develop and refine scheduling models using: Constraint Programming Mixed Integer Programming (MIP) Metaheuristic Algorithms (e.g., Genetic Algorithm, Ant Colony, Simulated Annealing) Reinforcement Learning or Deep Q-Learning Translate shop floor constraints (machines, manpower, sequence dependencies, changeovers) into mathematical models. Create simulation environments to test scheduling models under different scenarios. 2. Data Exploration & Feature Engineering Analyze structured and semi-structured production data from MES, SCADA, ERP, and other sources. Build pipelines for data preprocessing, normalization, and handling missing values. Perform feature engineering to capture important relationships like setup times, cycle duration, and bottlenecks. 3. Model Validation & Deployment Use statistical metrics and domain KPIs (e.g., throughput, utilization, makespan, WIP) to validate scheduling outcomes. Deploy solutions using APIs, dashboards (Streamlit, Dash), or via integration with existing production systems. Support ongoing maintenance, updates, and performance tuning of deployed models. 4. Collaboration & Stakeholder Engagement Work closely with production managers, planners, and domain experts to understand real-world constraints and validate model results. Document solution approaches, model assumptions, and provide technical training to stakeholders. Qualifications: Bachelor’s or Master’s degree in: Data Science, Computer Science, Industrial Engineering, Operations Research, Applied Mathematics, or equivalent. Minimum 1 year of experience in data science roles with exposure to: AI/ML pipelines, predictive modelling, Optimization techniques or industrial scheduling Proficiency in Python, especially with: pandas, numpy, scikit-learn ortools, pulp, cvxpy or other optimization libraries, matplotlib, plotly for visualization Solid understanding of: Production planning & control processes (dispatching rules, job-shop scheduling, etc.), Machine Learning fundamentals (regression, classification, clustering) Familiarity with version control (Git), Jupyter/VSCode environments, and CI/CD principles Preferred (Nice-to-Have) Skills: Experience with: Time-series analysis, sensor data, or anomaly detection, Manufacturing execution systems (MES), SCADA, PLC logs, or OPC UA data, Simulation tools (SimPy, Arena, FlexSim) or digital twin technologies Exposure to containerization (Docker) and model deployment (FastAPI, Flask) Understanding of lean manufacturing principles, Theory of Constraints, or Six Sigma Soft Skills: Strong problem-solving mindset with ability to balance technical depth and business context. Excellent communication and storytelling skills to convey insights to both technical and non-technical stakeholders. Eagerness to learn new tools, technologies, and domain knowledge.

Position Overview Job title: Debt Strategic Analytics Corporate Title: VP Location: Mumbai, India Role Description Deutsche CIB Centre Pvt Ltd is Deutsche bank’s global platform for front-office & aligned functions to create value by utilizing non-traditional locations, exceptional talent, and a collaborative culture. This branch of Deutsche Bank Group company is looking for extremely bright candidates for the role of Debt Strategic Analytics (Strats). The candidate is required to work in close collaboration with London/Singapore teams on various quantitative and regulatory driven projects. Candidate is required to understand the business problem, gather information required for the implementation and provide an end-to-end optimized solution on a scalable platform. Implementation of the project needs to be done in Python and C++ programming language. Candidate should possess excellent English communication skills to coordinate and communicate effectively with various stakeholders spread across the globe. What We’ll Offer You As part of our flexible scheme, here are just some of the benefits that you’ll enjoy Best in class leave policy Gender neutral parental leaves 100% reimbursement under childcare assistance benefit (gender neutral) Flexible working arrangements Employee Assistance Program for you and your family members Comprehensive Hospitalization Insurance for you and your dependents Accident and Term life Insurance Your Key Responsibilities The Quantitative Strategist (Quant Strat) is responsible for designing, developing and implementing through analytical (quantitative) and direct coding (e.g., via C++; Python or any other relevant application), quantitative strategic models, risk management (credit risk, market risk, anti-financial crime etc.) and pricing solutions to meet business & control needs and drive respective strategies or regulatory adherence. Some responsibilities for this role include, but not limited to: Work with Capital and Liquidity Management colleagues in Treasury to develop a framework to optimize funding for the bank, manage asset and liability mismatches in the liquidity pool and bring more transparency into the process. Development of complex processes, framework or risk analysis as well as improvements specially focused on Liquidity Risk. Implement, enhance and maintain existing framework to measure market risks across the bank from Treasury perspective. Remediation of regulatory as well as external and internal findings against any existing risk model. Build quant driven tools and products for front-office and control functions. Coordinate and gather information from various stakeholders for deeper understanding of the business. Develop and design tables and databases required for storage of the data. Design an automated solution which is optimized and scalable. Your Skills And Experience Strong programming skills in C++ and Python preferably in financial industry. Relevant experience of at least 8 years in Banking or Software Development. At least 3+ year experience with relational database design (oracle, mysql). Strong understanding of Data Structures & Algorithms, memory optimization etc. Good quantitative skills in Probability, Calculus, and Linear Algebra. Experience with applied econometrics (Hypothesis testing, PCA, Linear/Non-Linear Regression etc.) is a plus. Knowledge of applied linear/Integer linear programming, dynamic programming & greedy algorithms is a plus. Relevant experience in some of financial products like Bonds, Swaps, Cross Currency Swaps, Loans & Deposits is a plus. Strong communication skills and presentation ability with attention to detail. Good problem-solving instincts and strong analytical skills. Strong educational background in Engineering/Science, preferably from top tier colleges in India. How We’ll Support You Training and development to help you excel in your career. Flexible working to assist you balance your personal priorities. Coaching and support from experts in your team. A culture of continuous learning to aid progression. A range of flexible benefits that you can tailor to suit your needs. About Us And Our Teams Please visit our company website for further information: https://www.db.com/company/company.htm We strive for a culture in which we are empowered to excel together every day. This includes acting responsibly, thinking commercially, taking initiative and working collaboratively. Together we share and celebrate the successes of our people. Together we are Deutsche Bank Group. We welcome applications from all people and promote a positive, fair and inclusive work environment.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. Job Description: Senior AI Engineer (Tech Lead) Role Overview: We are seeking a highly skilled and experienced Senior AI Engineers with a minimum of 4 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Your technical responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Leading a team of 4-6 developers Assist in the development and implementation of AI models and systems, leveraging techniques such as Large Language Models (LLMs) and generative AI. Collaborate with stakeholders to identify business opportunities and define AI project goals. Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, Agentic Framework to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Minimum 4 years of experience in Python, Data Science, Machine Learning, OCR and document intelligence Experience in leading a team of 4-6 developers Demonstrated ability to conceptualize technical solutions, apply accurate estimation techniques, and effectively engage with customer stakeholders In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Strong knowledge of Python frameworks such as Django, Flask, or FastAPI. Experience with RESTful API design and development. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills: Understanding of agentic AI concepts and frameworks Proficiency in designing or interacting with agent-based AI architectures Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems. Utilize optimization tools and techniques, including MIP (Mixed Integer Programming). Drive DevOps and MLOps practices, covering continuous integration, deployment, and monitoring of AI models. Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. AI Engineer Role Overview: We are seeking a highly skilled and experienced AI Engineers with a minimum of 2 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Your technical responsibilities: Assist in the development and implementation of AI models and systems, leveraging techniques such as Large Language Models (LLMs) and generative AI. Design, develop, and maintain efficient, reusable, and reliable Python code Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, Agentic Framework to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Write unit tests and conduct code reviews to ensure high-quality, bug-free software. Troubleshoot and debug applications to optimize performance and fix issues. Work with databases (SQL, NoSQL) and integrate third-party APIs. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Minimum 2 years of experience in Python, Data Science, Machine Learning, OCR and document intelligence In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Strong knowledge of Python frameworks such as Django, Flask, or FastAPI. Experience with RESTful API design and development. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Good to Have Skills: Understanding of agentic AI concepts and frameworks Proficiency in designing or interacting with agent-based AI architectures Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems. Utilize optimization tools and techniques, including MIP (Mixed Integer Programming). Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. Senior Data Scientist Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 4 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Your technical responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI. Collaborate with stakeholders to identify business opportunities and define AI project goals. Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, Agentic Framework to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus. Minimum 4 years of experience in Data Science and Machine Learning. In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills: Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models Utilize optimization tools and techniques, including MIP (Mixed Integer Programming. Deep knowledge of classical AIML (regression, classification, time series, clustering) Drive DevOps and MLOps practices, covering CI/CD and monitoring of AI models. Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. EY GDS – Data and Analytics (D&A) – Senior – Senior Data Scientist Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 3 - 7 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Your technical responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI. Collaborate with stakeholders to identify business opportunities and define AI project goals. Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus. Minimum 3-7 years of experience in Data Science and Machine Learning. In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills: Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems. Utilize optimization tools and techniques, including MIP (Mixed Integer Programming). Drive DevOps and MLOps practices, covering continuous integration, deployment, and monitoring of AI models. Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. EY GDS – Data and Analytics (D&A) – Senior – Senior Data Scientist Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 3 - 7 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Your technical responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI. Collaborate with stakeholders to identify business opportunities and define AI project goals. Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus. Minimum 3-7 years of experience in Data Science and Machine Learning. In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills: Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems. Utilize optimization tools and techniques, including MIP (Mixed Integer Programming). Drive DevOps and MLOps practices, covering continuous integration, deployment, and monitoring of AI models. Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.