150 Arima Jobs - Page 3

About Us Thoucentric is the Consulting arm of Xoriant, a prominent digital engineering services company with 5000 employees. We are headquartered in Bangalore with presence across multiple locations in India, US, UK, Singapore & Australia Globally. As the Consulting business of Xoriant, we help clients with Business Consulting, Program & Project Management, Digital Transformation, Product Management, Process & Technology Solutioning and Execution including Analytics & Emerging Tech areas cutting across functional areas such as Supply Chain, Finance & HR, Sales & Distribution across US, UK, Singapore and Australia. Our unique consulting framework allows us to focus on execution rather than pure advisory. We are working closely with marquee names in the global consumer & packaged goods (CPG) industry, new age tech and start-up ecosystem. Xoriant (Parent entity) started in 1990 and is a Sunnyvale, CA headquartered digital engineering firm with offices in the USA, Europe, and Asia. Xoriant is backed by ChrysCapital, a leading private equity firm. Our strengths are now combined with Xoriants capabilities in AI & Data, cloud, security and operations services proven for 30 years. We have been certified as "Great Place to Work" by AIM and have been ranked as "50 Best Firms for Data Scientists to Work For." We have an experienced consulting team of over 450 world-class business and technology consultants based across six global locations, supporting clients through their expert insights, entrepreneurial approach and focus on delivery excellence. We have also built point solutions and products through Thoucentric labs using AI/ML in the supply chain space. Job Title: Data Scientist (3-5 Years Experience) Location: Bangalore About Us: Thoucentric is a forward-thinking organization at the forefront of leveraging data-driven insights to solve complex business challenges. We are seeking a passionate and skilled Data Scientist to join our dynamic team and help us drive innovation through advanced analytics and machine learning. Key Responsibilities: Develop and implement machine learning and deep learning models for various business problems, with a strong focus on time series forecasting. Analyze large, complex datasets to extract actionable insights and identify trends, patterns, and opportunities for improvement. Design, build, and validate predictive models using state-of-the-art techniques, ensuring scalability and robustness. Collaborate with cross-functional teams (Product, Engineering, Business) to translate business requirements into data science solutions. Communicate findings and recommendations clearly to both technical and non-technical stakeholders. Stay updated with the latest research and advancements in machine learning, deep learning, and time series analysis, and proactively apply new techniques as appropriate. Mentor junior team members and contribute to a culture of continuous learning and innovation. Requirements Required Skills & Qualifications: 3-5 years of hands-on experience in data science, machine learning, and statistical modeling. Strong expertise in time series forecasting (ARIMA, XGBoost, RandomForest, TFT, NHITS, etc.) and familiarity with deep learning frameworks (TensorFlow, PyTorch). Excellent programming skills in Python (preferred), with proficiency in libraries such as NumPy, Pandas, scikit-learn, and visualization tools (Matplotlib, Seaborn, Plotly). Solid conceptual understanding of machine learning algorithms, deep learning architectures, and statistical methods. Experience with data preprocessing, feature engineering, and model evaluation. Ability to learn quickly and adapt to new technologies, tools, and methodologies. Strong problem-solving skills and a keen attention to detail. Excellent communication and presentation skills. Preferred Qualifications: Experience with cloud platforms and MLOps tools. Exposure to big data technologies (Spark, Hadoop) is a plus. Masters degree in Computer Science, Statistics, Mathematics, or a related field. Benefits What a Consulting role at Thoucentric will offer you? Opportunity to define your career path and not as enforced by a manager A great consulting environment with a chance to work with Fortune 500 companies and startups alike. A dynamic but relaxed and supportive working environment that encourages personal development. Be part of One Extended Family. We bond beyond work - sports, get-togethers, common interests etc. Work in a very enriching environment with Open Culture, Flat Organization and Excellent Peer Group. Be part of the exciting Growth Story of Thoucentric! I'm interested Locations: Bangalore North, India | Posted on: 05/02/2025

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Senior Data Scientist We are the global technology company behind the world’s fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless®. We ensure every employee has the opportunity to be a part of something bigger and to change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities. The Mastercard Launch program is aimed at early career talent, to help you develop skills and gain cross-functional work experience. Over a period of 18 months, Launch participants will be assigned to a business unit, learn and develop skills, and gain valuable on the job experience. Mastercard has over 2 billion payment cards issued by 25,000+ banks across 190+ countries and territories, amassing over 10 petabytes of data. Millions of transactions are flowing to Mastercard in real-time providing an ideal environment to apply and leverage AI at scale. The AI team is responsible for building and deploying innovative AI solutions for all divisions within Mastercard securing a competitive advantage. Our objectives include achieving operational efficiency, improving customer experience, and ensuring robust value propositions of our core products (Credit, Debit, Prepaid) and services (recommendation engine, anti-money laundering, fraud risk management, cybersecurity) Role: Gather relevant information to define the business problem Creative thinker capable of linking AI methodologies to identified business challenges Develop AI/ML applications leveraging the latest industry and academic advancements Ability to work cross-functionally, and across borders drawing on a broader team of colleagues to effectively execute the AI agenda All About You: :• Demonstrated passion for AI competing in sponsored challenges such as Kaggle Previous experience with or exposure to: Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL • •Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, •Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, •Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil •Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s is a plus. •Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost •Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono is a plus Concentration in Computer Science Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Manager, Data Scientist Our Vision AI Garage is responsible for establishing Mastercard as an AI powerhouse. AI will be leveraged and implemented at scale within Mastercard providing a foundational, competitive advantage for the future. All internal processes, all products and services will be enabled by AI continuously advancing our value proposition, consumer experience, and efficiency. Opportunity Join Mastercard's AI Garage @ Gurgaon, a newly created strategic business unit executing on identified use cases for product optimization and operational efficiency securing Mastercard's competitive advantage through all things AI. The AI professional will be responsible for the creative application and execution of AI use cases, working collaboratively with other AI professionals and business stakeholders to effectively drive the AI mandate. Role Ensure all AI solution development is in line with industry standards for data management and privacy compliance including the collection, use, storage, access, retention, output, reporting, and quality of data at Mastercard Adopt a pragmatic approach to AI, capable of articulating complex technical requirements in a manner this is simple and relevant to stakeholder use cases Gather relevant information to define the business problem interfacing with global stakeholders Creative thinker capable of linking AI methodologies to identified business challenges Identify commonalities amongst use cases enabling a microservice approach to scaling AI at Mastercard, building reusable, multi-purpose models Develop AI/ML solutions/applications leveraging the latest industry and academic advancements Leverage open and closed source technologies to solve business problems Ability to work cross-functionally, and across borders drawing on a broader team of colleagues to effectively execute the AI agenda Partner with technical teams to implement developed solutions/applications in production environment Support a learning culture continuously advancing AI capabilities All About You Experience 3+ years of experience in the Data Sciences field with a focus on AI strategy and execution and developing solutions from scratch Demonstrated passion for AI competing in sponsored challenges such as Kaggle Previous experience with or exposure to: o Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL o Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning o Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil o Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s is a plus. Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing o Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono is a plus Exposure or experience using collaboration tools such as: o Confluence (Documentation) o Bitbucket/Stash (Code Sharing) o Shared Folders (File Sharing) o ALM (Project Management) Knowledge of payments industry a plus Experience with SAFe (Scaled Agile Framework) process is a plus Effectiveness Effective at managing and validating assumptions with key stakeholders in compressed timeframes, without hampering development momentum Capable of navigating a complex organization in a relentless pursuit of answers and clarity Enthusiasm for Data Sciences embracing the creative application of AI techniques to improve an organization's effectiveness Ability to understand technical system architecture and overarching function along with interdependency elements, as well as anticipate challenges for immediate remediation Ability to unpack complex problems into addressable segments and evaluate AI methods most applicable to addressing the segment Incredible attention to detail and focus instilling confidence without qualification in developed solutions Core Capabilities Strong written and oral communication skills Strong project management skills Concentration in Computer Science Some international travel required Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

Job Description for Data Scientist (Analytics): Work Experience: 12+ years Location: Hyderabad Requirements: Must have supply chain experience with ML, especially in Demand Planning and Retail Merchandise Financial Planning Strong expertise in Time Series Forecasting using: Statistical models: ARIMA, Exponential Smoothing, Prophet ML models: XGBoost, LightGBM, etc. Minimum 1 year of hands-on o9 platform experience Proficient in Python/R and relevant data science libraries Experience in building end-to-end ML pipelines for forecasting Strong understanding of demand drivers and retail planning cycles Excellent communication and cross-functional collaboration skills Experience required: 6+ years for Architects Arima, Lightgbm, Xgboost, Prophet

Job Description for Data Scientist (Analytics): Work Experience: 12+ years Location: Hyderabad Requirements: Must have supply chain experience with ML, especially in Demand Planning and Retail Merchandise Financial Planning Strong expertise in Time Series Forecasting using: Statistical models: ARIMA, Exponential Smoothing, Prophet ML models: XGBoost, LightGBM, etc. Minimum 1 year of hands-on o9 platform experience Proficient in Python/R and relevant data science libraries Experience in building end-to-end ML pipelines for forecasting Strong understanding of demand drivers and retail planning cycles Excellent communication and cross-functional collaboration skills Experience required: 6+ years for Architects

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title And Summary Manager, Data Scientist Our Vision AI Garage is responsible for establishing Mastercard as an AI powerhouse. AI will be leveraged and implemented at scale within Mastercard providing a foundational, competitive advantage for the future. All internal processes, all products and services will be enabled by AI continuously advancing our value proposition, consumer experience, and efficiency. Opportunity Join Mastercard's AI Garage @ Gurgaon, a newly created strategic business unit executing on identified use cases for product optimization and operational efficiency securing Mastercard's competitive advantage through all things AI. The AI professional will be responsible for the creative application and execution of AI use cases, working collaboratively with other AI professionals and business stakeholders to effectively drive the AI mandate. Role Ensure all AI solution development is in line with industry standards for data management and privacy compliance including the collection, use, storage, access, retention, output, reporting, and quality of data at Mastercard Adopt a pragmatic approach to AI, capable of articulating complex technical requirements in a manner this is simple and relevant to stakeholder use cases Gather relevant information to define the business problem interfacing with global stakeholders Creative thinker capable of linking AI methodologies to identified business challenges Identify commonalities amongst use cases enabling a microservice approach to scaling AI at Mastercard, building reusable, multi-purpose models Develop AI/ML solutions/applications leveraging the latest industry and academic advancements Leverage open and closed source technologies to solve business problems Ability to work cross-functionally, and across borders drawing on a broader team of colleagues to effectively execute the AI agenda Partner with technical teams to implement developed solutions/applications in production environment Support a learning culture continuously advancing AI capabilities Experience All About You 3+ years of experience in the Data Sciences field with a focus on AI strategy and execution and developing solutions from scratch Demonstrated passion for AI competing in sponsored challenges such as Kaggle Previous experience with or exposure to: Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s is a plus. Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono is a plus Exposure or experience using collaboration tools such as: Confluence (Documentation) Bitbucket/Stash (Code Sharing) Shared Folders (File Sharing) ALM (Project Management) Knowledge of payments industry a plus Experience with SAFe (Scaled Agile Framework) process is a plus Effectiveness Effective at managing and validating assumptions with key stakeholders in compressed timeframes, without hampering development momentum Capable of navigating a complex organization in a relentless pursuit of answers and clarity Enthusiasm for Data Sciences embracing the creative application of AI techniques to improve an organization's effectiveness Ability to understand technical system architecture and overarching function along with interdependency elements, as well as anticipate challenges for immediate remediation Ability to unpack complex problems into addressable segments and evaluate AI methods most applicable to addressing the segment Incredible attention to detail and focus instilling confidence without qualification in developed solutions Core Capabilities Strong written and oral communication skills Strong project management skills Concentration in Computer Science Some international travel required Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines. 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Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title And Summary Senior Data Scientist We are the global technology company behind the world’s fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless®. We ensure every employee has the opportunity to be a part of something bigger and to change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities. The Mastercard Launch program is aimed at early career talent, to help you develop skills and gain cross-functional work experience. Over a period of 18 months, Launch participants will be assigned to a business unit, learn and develop skills, and gain valuable on the job experience. Mastercard has over 2 billion payment cards issued by 25,000+ banks across 190+ countries and territories, amassing over 10 petabytes of data. Millions of transactions are flowing to Mastercard in real-time providing an ideal environment to apply and leverage AI at scale. The AI team is responsible for building and deploying innovative AI solutions for all divisions within Mastercard securing a competitive advantage. Our objectives include achieving operational efficiency, improving customer experience, and ensuring robust value propositions of our core products (Credit, Debit, Prepaid) and services (recommendation engine, anti-money laundering, fraud risk management, cybersecurity) Role Gather relevant information to define the business problem Creative thinker capable of linking AI methodologies to identified business challenges Develop AI/ML applications leveraging the latest industry and academic advancements Ability to work cross-functionally, and across borders drawing on a broader team of colleagues to effectively execute the AI agenda All About You : Demonstrated passion for AI competing in sponsored challenges such as Kaggle Previous experience with or exposure to: Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s is a plus. Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono is a plus Concentration in Computer Science Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines. R-252120

Immediate DS Role Requirements – Key Skillset Must have supply chain experience with ML, especially inDemand Planning andRetail Merchandise Financial Planning Strong expertise inTime Series Forecasting using: Statistical models:ARIMA,Exponential Smoothing,Prophet ML models:XGBoost,LightGBM, etc. Minimum1 year of hands-on o9 platform experience Proficient inPython/R and relevant data science libraries Experience in buildingend-to-end ML pipelines for forecasting Strong understanding ofdemand drivers and retail planning cycles Excellent communication and cross-functional collaboration skills Experience required: 10+ years for Architects

Description AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help. You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. Do you love problem solving? Are you looking for real world Supply Chain challenges? Do you have a desire to make a major contribution to the future, in the rapid growth environment of Cloud Computing? Amazon Web Services is looking for a highly motivated, Data Scientist to help build scalable, predictive and prescriptive business analytics solutions that supports AWS Supply Chain and Procurement organization. You will be part of the Supply Chain Analytics team working with Global Stakeholders, Data Engineers, Business Intelligence Engineers and Business Analysts to achieve our goals. We are seeking an innovative and technically strong data scientist with a background in optimization, machine learning, and statistical modeling/analysis. This role requires a team member to have strong quantitative modeling skills and the ability to apply optimization/statistical/machine learning methods to complex decision-making problems, with data coming from various data sources. The candidate should have strong communication skills, be able to work closely with stakeholders and translate data-driven findings into actionable insights. The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and ability to work in a fast-paced and ever-changing environment. Key job responsibilities Demonstrate thorough technical knowledge on feature engineering of massive datasets, effective exploratory data analysis, and model building using industry standard time Series Forecasting techniques like ARIMA, ARIMAX, Holt Winter and formulate ensemble model. Proficiency in both Supervised(Linear/Logistic Regression) and UnSupervised algorithms(k means clustering, Principle Component Analysis, Market Basket analysis). Experience in solving optimization problems like inventory and network optimization . Should have hands on experience in Linear Programming. Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area Detail-oriented and must have an aptitude for solving unstructured problems. You should work in a self-directed environment, own tasks and drive them to completion. Excellent business and communication skills to be able to work with business owners to develop and define key business questions and to build data sets that answer those questions Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers About The Team Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Basic Qualifications 5+ years of data scientist experience 4+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience Experience applying theoretical models in an applied environment Preferred Qualifications Experience in Python, Perl, or another scripting language Experience in a ML or data scientist role with a large technology company Functional knowledge of AWS platforms such as S3, Glue, Athena, Sagemaker, Lambda, EC2, Batch, Step Function. Experience in creating powerful data driven visualizations to describe your ML modeling results to stakeholders Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Company - ADSIPL - Karnataka Job ID: A2954457

Senior Data Science Lead Primary Skills Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio Job requirements JD is below: The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. Key Responsibilities Architecting & Scaling Agentic AI Solutions Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. Hands-On Development & Optimization Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. Driving AI Innovation & Research Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. AI Strategy & Business Impact Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. Mentorship & Capability Building Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents.

We are seeking a talented and versatile Analytics & AI Specialist to join our dynamic team. This role combines expertise in General Analytics, Artificial Intelligence (AI), Generative AI (GenAI), forecasting techniques, and client management to deliver innovative solutions that drive business success. The ideal candidate will work closely with clients, leverage AI technologies to enhance data-driven decision-making, and apply forecasting models to predict business trends and outcomes. AI & Machine Learning: Experience with machine learning frameworks and libraries (e.g., TensorFlow, scikit-learn, PyTorch, Keras). Knowledge of Generative AI (GenAI) tools and technologies, including GPT models, GANs (Generative Adversarial Networks), and transformer models. Familiarity with AI cloud platforms (e.g., Google AI, AWS SageMaker, Azure AI). Forecasting: Expertise in time series forecasting methods (e.g., ARIMA, Exponential Smoothing, Prophet) and machine learning-based forecasting models. Experience applying predictive analytics and building forecasting models for demand, sales, and resource planning. Data Visualization & Reporting: Expertise in creating interactive reports and dashboards with tools like Tableau, Power BI, or Google Data Studio. Ability to present complex analytics and forecasting results in a clear and compelling way to stakeholders. Client Management & Communication: Strong client-facing skills with the ability to manage relationships and communicate complex technical concepts to non-technical audiences. Ability to consult and guide clients on best practices for implementing AI-driven solutions. Excellent written and verbal communication skills for client presentations, technical documentation, and report writing. Additional Skills: Project Management: Experience managing data analytics projects from inception to completion, ensuring deadlines and objectives are met. Cloud Platforms: Experience with cloud platforms (AWS, GCP, Azure) for deploying AI models, handling large datasets, and performing distributed computing. Business Acumen: A strong understanding of business KPIs and the ability to align AI and analytics projects with client business goals.

Job Title: Senior Data Scientist IT/OT, SAP & Classical ML Experience: 7+ Years Location: Jaipur / Noida / Pune Employment Type: Full-time About the Role: We are seeking an experienced Senior Data Scientist with a strong background in IT/OT systems, SAP integration, and classical machine learning techniques. The ideal candidate will bring deep technical expertise combined with a consulting mindset and excellent communication skills. This role involves working closely with clients to understand complex business challenges and delivering actionable AI/ML-driven solutions across diverse enterprise environments. Key Responsibilities: Develop and implement classical machine learning models tailored for IT/OT and SAP data environments. Collaborate with clients and cross-functional teams to analyze requirements and translate them into scalable data science solutions. Integrate data from SAP and other enterprise systems to build comprehensive predictive and prescriptive analytics. Conduct exploratory data analysis, feature engineering, model training, validation, and deployment. Present findings, insights, and recommendations clearly to both technical and non-technical stakeholders. Support digital transformation initiatives leveraging data-driven decision-making in IT and OT operations. Stay updated on industry trends, emerging technologies, and best practices in data science and enterprise IT/OT systems. Required Qualifications: 7+ years of professional experience in Data Science, focusing on classical ML techniques. Proven experience working with IT/OT systems and integrating SAP data into analytic workflows. Strong proficiency in Python/R and ML frameworks such as scikit-learn, TensorFlow, or PyTorch. Demonstrated consulting experience with client-facing engagements and solution delivery. Excellent communication and presentation skills to convey complex concepts effectively. Bachelors or Master’s degree in Computer Science, Engineering, Data Science, or related fields. Preferred Qualifications: Hands-on experience with SAP modules and data structures. Knowledge of IoT and industrial OT systems. Experience with cloud platforms (AWS, Azure, GCP) and MLOps. Familiarity with data visualization tools like Power BI or Tableau. What We Offer: Exciting opportunity to work on digital transformation projects in leading enterprises. Collaborative work culture across Jaipur, Noida, and Pune offices. Competitive salary and benefits package. Continuous learning and professional growth opportunities. If this sounds like you, we'd love to connect! Apply now to chaity.mukherjee@celebaltech.com to be part of our innovative team.

Develop and implement sales forecasting models using advanced time series forecasting algorithms. Analyze and optimize pricing strategies to maximize revenue and profitability. Proven experience with time series forecasting techniques (ARIMA, Prophet, LSTM, etc.). Pandas, NumPy, Scikit-learn Proven experience with time series forecasting techniques (ARIMA, Prophet, LSTM, etc.). Expertise in sales forecasting and pricing strategy in a retail context. Location : - Remote,New Delhi,Mumbai,Bengaluru

Entity :- Accenture Strategy & Consulting Job location :- Mumbai About S&C - Global Network :- Accenture Global Network - Data & AI practice help our clients grow their business in entirely new ways. Analytics enables our clients to achieve high performance through insights from data - insights that inform better decisions and strengthen customer relationships. From strategy to execution, Accenture works with organizations to develop analytic capabilities - from accessing and reporting on data to predictive modelling - to outperform the competition WHATS IN IT FOR YOU Accenture CFO & EV team under Data & AI team has comprehensive suite of capabilities in Risk, Fraud, Financial crime, and Finance. Within risk realm, our focus revolves around the model development, model validation, and auditing of models. Additionally, our work extends to ongoing performance evaluation, vigilant monitoring, meticulous governance, and thorough documentation of models. Get to work with top financial clients globally Access resources enabling you to utilize cutting-edge technologies, fostering innovation with the worlds most recognizable companies. Accenture will continually invest in your learning and growth and will support you in expanding your knowledge. Youll be part of a diverse and vibrant team collaborating with talented individuals from various backgrounds and disciplines continually pushing the boundaries of business capabilities, fostering an environment of innovation. What you would do in this role Engagement Execution Work independently/with minimal supervision in client engagements that may involve model development, validation, governance, strategy, transformation, implementation and end-to-end delivery of risk solutions for Accentures clients. Ability to manage workstream of large projects / small projects with responsibilities of managing quality of deliverables for junior team members. Demonstrated ability of managing day to day interactions with the Client stakeholders Practice Enablement Guide junior team members. Support development of the Practice by driving innovations, initiatives. Develop thought capital and disseminate information around current and emerging trends in Risk. Qualification Who we are looking for 7 - 12 years of relevant Risk Analytics experience at one or more Financial Services firms, or Professional Services / Risk Advisory with significant exposure to one or more of the following areas: Development, validation, and audit of: Credit Risk- PD/LGD/EAD Models, CCAR/DFAST Loss Forecasting and Revenue Forecasting Models, IFRS9/CECL Loss Forecasting Models across Retail and Commercial portfolios Credit Acquisition/Behavior/Collections/Recovery Modeling and Strategies, Credit Policies, Limit Management, Acquisition Frauds, Collections Agent Matching/Channel Allocations across Retail and Commercial portfolios Regulatory Capital and Economic Capital Models Liquidity Risk Liquidity models, stress testing models, Basel Liquidity reporting standards Anti Money Laundering AML scenarios/alerts, Network Analysis Operational risk AMA modeling, operational risk reporting Conceptual understanding of Basel/CCAR/DFAST/CECL/IFRS9 and other risk regulations Experience in conceptualizing and creating risk reporting and dashboarding solutions. Experience in modeling with statistical techniques such as linear regression, logistic regression, GLM, GBM, XGBoost, CatBoost, Neural Networks, Time series ARMA/ARIMA, ML interpretability and bias algorithms etc. Programing Languages - SAS, R, Python, Spark, Scala etc., Tools such as Tableau, QlikView, PowerBI, SAS VA etc. Strong understanding of Risk function and ability to apply them in client discussions and project implementation. Academic : Masters degree in a quantitative discipline mathematics, statistics, economics, financial engineering, operations research or related field or MBA from top-tier universities. Strong academic credentials and publications, if applicable. Industry certifications such as FRM, PRM, CFA preferred. Excellent communication and interpersonal skills. Accenture is an equal opportunities employer and welcomes applications from all sections of society and does not discriminate on grounds of race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, or any other basis as protected by applicable law.

Job Title : Real Estate Business Analysts Experience : 5-10 Years Location : Bangalore Key Responsibilities: Gather stakeholder requirements through interviews, workshops, and surveys to ensure alignment and broad input. Create clear BRDs and FRDs, write use-cases and user stories, and develop process maps to visualize business workflows. Apply critical thinking to assess scenarios, evaluate options transparently, and make informed, risk-aware decisions with stakeholder input. Develop project plans and schedules, manage risks, and apply Agile methods like Scrum to deliver requirements. Gather and document business needs, create business cases, and collaborate with cross-functional teams to implement solutions. Drive process and system improvements, bridge IT and business teams for successful outcomes, and provide end-user training and support. Requisites: Analyze data to uncover insights, identify gaps, and propose solutions. Conduct SWOT analyses to evaluate and guide business decisions. Proficient in linear and logistic regression, time series forecasting (e.g., moving averages, exponential smoothing, ARIMA), and distinguishing correlation from causation using correlation coefficients. Skilled in creating prototypes and wireframes for requirement validation, and using SQL to extract and analyze relevant data. Proficient in using data visualizations-charts, graphs, heatmaps, geospatial visuals, pivot tables, and interactive dashboards-to convey insights effectively and tell compelling stories. Expert in Power BI, Land Vision, and Excel. Proficient in SQL, Python, and R for analysis and visualization. Familiar with modeling techniques like UML and BPMN.

Job Summary JD- Having a background in Retail will be a Big Plus. Advanced in Python: Proven experience with core libraries like pandas, numpy, scikit-learn, and matplotlib, plus advanced tools like statsmodels, xgboost, lightgbm, prophet, deep learning-based forecasting (e.g. Neural forecast). Advanced Forecasting Techniques: Experience with ensemble models, hierarchical forecasting, probabilistic forecasting, and multivariate time series. Pricing Models: Background in price elasticity modeling, good to have experience in optimization - at least one of the 2 resources Model Evaluation: Familiarity with time-series cross-validation, backtesting, and metrics such as MAE, MAPE, RMSE, SMAPE, and prediction intervals. SQL Proficiency: Ability to query and manage data from relational databases. Version Control: Comfortable working with Git/GitHub for collaboration and code management. Mandate skill- python and advanced forcesting skills with models like arima etc. Any shift- Second shift Location-Banglore

Job Summary JD- Having a background in Retail will be a Big Plus. Advanced in Python: Proven experience with core libraries like pandas, numpy, scikit-learn, and matplotlib, plus advanced tools like statsmodels, xgboost, lightgbm, prophet, deep learning-based forecasting (e.g. Neural forecast). Advanced Forecasting Techniques: Experience with ensemble models, hierarchical forecasting, probabilistic forecasting, and multivariate time series. Pricing Models: Background in price elasticity modeling, good to have experience in optimization - at least one of the 2 resources Model Evaluation: Familiarity with time-series cross-validation, backtesting, and metrics such as MAE, MAPE, RMSE, SMAPE, and prediction intervals. SQL Proficiency: Ability to query and manage data from relational databases. Version Control: Comfortable working with Git/GitHub for collaboration and code management. Mandate skill- python and advanced forcesting skills with models like arima etc. Any shift- Second shift Location-Banglore

Senior AI/ML Engineer - R01551337 Senior Data Science Lead Primary Skills Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio Job requirements JD is below: The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. ________________________________________ Key Responsibilities 1. Architecting & Scaling Agentic AI Solutions • Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. • Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. • Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. 2. Hands-On Development & Optimization • Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. • Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. • Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. 3. Driving AI Innovation & Research • Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. • Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. • Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. 4. AI Strategy & Business Impact • Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. • Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. 5. Mentorship & Capability Building • Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. • Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents. ________________________________________

At PwC, our people in data and analytics focus on leveraging data to drive insights and make informed business decisions. They utilise advanced analytics techniques to help clients optimise their operations and achieve their strategic goals. In data analysis at PwC, you will focus on utilising advanced analytical techniques to extract insights from large datasets and drive data-driven decision-making. You will leverage skills in data manipulation, visualisation, and statistical modelling to support clients in solving complex business problems. Years of Experience: Candidates with 4+ years of hands on experience Position: Senior Associate Required Skills: Successful candidates will have demonstrated the following skills and characteristics: Must Have Proven expertise in supply chain analytics across domains such as demand forecasting, inventory optimization, logistics, segmentation, and network design Well versed and hands-on experience of working on optimization methods like linear programming, mixed integer programming, scheduling optimization. Having understanding of working on third party optimization solvers like Gurobi will be an added advantage Proficiency in forecasting techniques (e.g., Holt-Winters, ARIMA, ARIMAX, SARIMA, SARIMAX, FBProphet, NBeats) and machine learning techniques (supervised and unsupervised) Strong command of statistical modeling, testing, and inference Proficient in using GCP tools: BigQuery, Vertex AI, Dataflow, Looker Building data pipelines and models for forecasting, optimization, and scenario planning Strong SQL and Python programming skills; experience deploying models in GCP environment Knowledge of orchestration tools like Cloud Composer (Airflow) Nice To Have Familiarity with MLOps, containerization (Docker, Kubernetes), and orchestration tools (e.g., Cloud composer) Strong communication and stakeholder engagement skills at the executive level Roles And Responsibilities Assist analytics projects within the supply chain domain, driving design, development, and delivery of data science solutions Develop and execute on project & analysis plans under the guidance of Project Manager Interact with and advise consultants/clients in US as a subject matter expert to formalize data sources to be used, datasets to be acquired, data & use case clarifications that are needed to get a strong hold on data and the business problem to be solved Drive and Conduct analysis using advanced analytics tools and coach the junior team members Implement necessary quality control measures in place to ensure the deliverable integrity like data quality, model robustness, and explainability for deployments. Validate analysis outcomes, recommendations with all stakeholders including the client team Build storylines and make presentations to the client team and/or PwC project leadership team Contribute to the knowledge and firm building activities Professional And Educational Background BE / B.Tech / MCA / M.Sc / M.E / M.Tech /Master’s Degree /MBA from reputed institute

Senior AI/ML Engineer Primary Skills Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio Job requirements Key Responsibilities Develop and optimize machine learning models for various applications. Implement AI algorithms, including deep learning, neural networks, and natural language processing (NLP). Design and maintain data pipelines for model training and deployment. Collaborate with cross-functional teams to integrate AI solutions into products. Conduct research on emerging AI technologies and best practices. Ensure scalability, reliability, and efficiency of AI models in production environments. Troubleshoot and improve existing AI/ML systems. Required Skills & Qualifications - Experience: 3-8 years in AI/ML development. Technical Skills: Proficiency in Python, TensorFlow, PyTorch, and other ML frameworks. Data Handling: Strong knowledge of data preprocessing, feature engineering, and model evaluation. Cloud & Deployment: Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes). Problem-Solving: Ability to analyze complex problems and develop AI-driven solutions.

Senior Data Science Lead Primary Skills Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio Job requirements JD is below: The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. ________________________________________ Key Responsibilities 1. Architecting & Scaling Agentic AI Solutions Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. 2. Hands-On Development & Optimization Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. 3. Driving AI Innovation & Research Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. 4. AI Strategy & Business Impact Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. 5. Mentorship & Capability Building Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents. ________________________________________

Senior Data Science Lead Primary Skills Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio Job requirements JD is below: The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. ________________________________________ Key Responsibilities 1. Architecting & Scaling Agentic AI Solutions Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. 2. Hands-On Development & Optimization Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. 3. Driving AI Innovation & Research Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. 4. AI Strategy & Business Impact Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. 5. Mentorship & Capability Building Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents. ________________________________________

Lead AI Engineer (GenAI & AI/ML Focus) Experience : 5+ years (with at least 1-2 years in GenAI/Agentic AI) About the Role We’re seeking a Lead AI Engineer to drive the design, development, and scaling of intelligent systems powered by Generative AI and Agentic AI architectures. You’ll play a central role in shaping the technical foundation of our AI solutions, guiding junior engineers, leading experiments, and taking end-to-end ownership of core components such as prompt infrastructure, model routing logic, RAG systems, and autonomous agents. This role blends deep hands-on engineering with architectural vision and technical leadership. If you’re someone who enjoys building fast, mentoring others, and solving tough AI problems across infrastructure, experimentation, and deployment, this role is for you. What You’ll Do Architect and implement LLM-based GenAI systems: prompt pipelines, retrieval flows, model selection, fallback mechanisms, prompt optimization, prompt versioning, modular prompts, and tool orchestration strategies Design and lead multi-agent systems with memory, tool-use, task adaptation, and inter-agent collaboration using frameworks like LangGraph, CrewAI, AutoGen, LangChain Architect, deploy, and continuously evolve retrieval-augmented generation (RAG) pipelines, handle model substitution, evaluation, prompt versioning, and runtime fallback strategies Drive the creation of internal prompt libraries, LLM orchestration logic, and evaluation harnesses Collaborate on fine-tuning, embedding optimization, and multi-model substitution workflows Collaborate on model evaluation, benchmarking, and agentic stress testing in production Own end-to-end AI applications involving image, text, tabular, and signal data, working with both unimodal and multi-modal inputs Develop solutions in computer vision (e.g., OCR, classification, object detection) and multi-modal AI (e.g., text + image pipelines) Design and evaluate statistical models and classical ML systems, including time-series forecasting and predictive analytics Lead development of signal processing or anomaly detection systems (e.g., for sensor, telemetry, or log data) Build & support cost-optimization, compliance, and monitoring in GenAI deployments Mentor and unblock junior AI engineers and interns through code reviews, architecture sessions, and contribute to team knowledge base and enforce internal best practices Work closely with product, data, and platform teams to bring AI prototypes to production Partner with Data, Product, and Platform teams to ship enterprise-grade AI modules Who Should Apply You are a builder with: Minimum qualification: Bachelor’s or Master’s degree Disciplines: Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Engineering, Physics, Statistics, Finance, Economics, or any other quantitative or computational field 5+ years of professional experience in AI/ML development (with 1-2+ years in GenAI or Agentic systems) Proven experience architecting and shipping real-world AI applications at scale Strong mentoring skills and a product-focused engineering mindset An interest in shaping the long-term AI strategy while delivering short-term wins Must-Have Skills Strong Python development skills with emphasis on modular, testable design Deep familiarity with LLM APIs, prompt frameworks (e.g., LangChain, LangGraph, CrewAI), and RAG pipelines Experience building systems for model selection, prompt fallback, tool orchestration, and versioning Familiarity with MLOps pipelines, ML monitoring, and model lifecycle management Experience in building and optimizing RAG architectures and vector database integrations Knowledge of model selection, fallbacks, prompt versioning, and task decomposition Strong grasp of autonomous agent design, reasoning, tool use, and multi-agent protocols Exposure to near real-time signal/voice processing, filtering, or event-based triggers (e.g., anomaly/event detection) Practical knowledge of statistical modeling, forecasting (ARIMA, Prophet, ML-based), or classical ML pipelines 5+ years in AI/ML engineering, including 1–2+ years working directly on GenAI/Agentic AI projects Preferred Skills Experience with multi-agent collaboration patterns (e.g., A2A, MCP) Deployment experience on GenAI platforms like OpenAI, Claude, Vertex AI, Amazon Bedrock, or Azure GenAI Familiarity with serverless deployment, egress handling, and LLM usage constraints Exposure to evaluation frameworks, benchmarking suites, or internal testing systems Experience integrating AI flows with external systems (BI tools, dashboards, APIs, automation platforms) Prior experience designing multi-modal pipelines combining vision, text, or sensor data Bonus / Good-to-Have Hands-on with fine-tuning workflows (LoRA, PEFT, etc.) Understanding of vector database internals and hybrid retrieval methods Familiarity with CV/multi-modal AI workflows using OpenCV, TorchVision, CLIP, BLIP, or related libraries Prior experience contributing to open-source GenAI libraries or leading internal tool development Why Join Us Help shape the next-gen AI stack from the ground up Work in a fast-moving environment with real ownership and autonomy Collaborate with a cross-disciplinary team at the cutting edge of GenAI and agentic systems Be part of an organization that values experimentation, modularity, and long-term impact Interested candidates can share the CV on shikha.rana@antiersolutions.com Job Type: Full-time Pay: ₹800,000.00 - ₹1,500,000.00 per year Benefits: Paid sick time Provident Fund Schedule: Day shift Monday to Friday Morning shift Application Question(s): How many days of Notice period you have? Experience: Machine learning: 4 years (Preferred) Team management: 4 years (Preferred) Location: Mohali, Punjab (Required) Work Location: In person

Licious is a fast-paced, innovative D2C brand revolutionizing the meat and seafood industry in India. We leverage cutting-edge technology, data science, and customer insights to deliver unmatched quality, convenience, and personalization. Join us to solve complex problems at scale and drive data-driven decision-making! Role Overview: We are seeking a Senior Data Scientist with 6+ years of experience to build and deploy advanced ML models (LLMs, Recommendation Systems, Demand Forecasting) and generate actionable insights. You will collaborate with cross-functional teams (Product, Supply Chain, Marketing) to optimize customer experience, demand prediction, and business growth. Key Responsibilities: 1. Machine Learning & AI Solutions: Develop and deploy Large Language Models (LLMs) for customer support automation, personalized content generation, and sentiment analysis. Enhance Recommendation Systems (collaborative filtering, NLP-based, reinforcement learning) to drive engagement and conversions. Build scalable Demand Forecasting models (time series, causal inference) to optimize inventory and supply chain. 2. Data-Driven Insights: Analyze customer behavior, transactional data, and market trends to uncover growth opportunities. Create dashboards and reports (using Tableau/Power BI) to communicate insights to stakeholders. 3. Cross-Functional Collaboration: Partner with Engineering to productionize models (MLOps, APIs, A/B testing). Work with Marketing to design hyper-personalized campaigns using CLV, churn prediction, and segmentation. 4. Innovation & Scalability: Stay updated with advancements in GenAI, causal ML, and optimization techniques. Improve model performance through feature engineering, ensemble methods, and experimentation. Qualifications: Education: BTech/MTech/MS/Ph.D. in Computer Science, Statistics, or related fields. Experience: 6+ years in Data Science, with hands-on expertise in: LLMs (GPT, BERT, fine-tuning, prompt engineering). Recommendation Systems (matrix factorization, neural CF, graph-based). Demand Forecasting (ARIMA, Prophet, LSTM, Bayesian methods). Python/R , SQL, PySpark, and ML frameworks (TensorFlow, PyTorch, scikit-learn). Cloud platforms (AWS/GCP) and MLOps tools (MLflow, Kubeflow).

Lead AI Engineer (GenAI & AI/ML Focus) Experience : 5+ years (with at least 1-2 years in GenAI/Agentic AI) About the Role We’re seeking a Lead AI Engineer to drive the design, development, and scaling of intelligent systems powered by Generative AI and Agentic AI architectures. You’ll play a central role in shaping the technical foundation of our AI solutions, guiding junior engineers, leading experiments, and taking end-to-end ownership of core components such as prompt infrastructure, model routing logic, RAG systems, and autonomous agents. This role blends deep hands-on engineering with architectural vision and technical leadership. If you’re someone who enjoys building fast, mentoring others, and solving tough AI problems across infrastructure, experimentation, and deployment, this role is for you. What You’ll Do Architect and implement LLM-based GenAI systems: prompt pipelines, retrieval flows, model selection, fallback mechanisms, prompt optimization, prompt versioning, modular prompts, and tool orchestration strategies Design and lead multi-agent systems with memory, tool-use, task adaptation, and inter-agent collaboration using frameworks like LangGraph, CrewAI, AutoGen, LangChain Architect, deploy, and continuously evolve retrieval-augmented generation (RAG) pipelines, handle model substitution, evaluation, prompt versioning, and runtime fallback strategies Drive the creation of internal prompt libraries, LLM orchestration logic, and evaluation harnesses Collaborate on fine-tuning, embedding optimization, and multi-model substitution workflows Collaborate on model evaluation, benchmarking, and agentic stress testing in production Own end-to-end AI applications involving image, text, tabular, and signal data, working with both unimodal and multi-modal inputs Develop solutions in computer vision (e.g., OCR, classification, object detection) and multi-modal AI (e.g., text + image pipelines) Design and evaluate statistical models and classical ML systems, including time-series forecasting and predictive analytics Lead development of signal processing or anomaly detection systems (e.g., for sensor, telemetry, or log data) Build & support cost-optimization, compliance, and monitoring in GenAI deployments Mentor and unblock junior AI engineers and interns through code reviews, architecture sessions, and contribute to team knowledge base and enforce internal best practices Work closely with product, data, and platform teams to bring AI prototypes to production Partner with Data, Product, and Platform teams to ship enterprise-grade AI modules Who Should Apply You are a builder with: Minimum qualification: Bachelor’s or Master’s degree Disciplines: Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Engineering, Physics, Statistics, Finance, Economics, or any other quantitative or computational field 5+ years of professional experience in AI/ML development (with 1-2+ years in GenAI or Agentic systems) Proven experience architecting and shipping real-world AI applications at scale Strong mentoring skills and a product-focused engineering mindset An interest in shaping the long-term AI strategy while delivering short-term wins Must-Have Skills Strong Python development skills with emphasis on modular, testable design Deep familiarity with LLM APIs, prompt frameworks (e.g., LangChain, LangGraph, CrewAI), and RAG pipelines Experience building systems for model selection, prompt fallback, tool orchestration, and versioning Familiarity with MLOps pipelines, ML monitoring, and model lifecycle management Experience in building and optimizing RAG architectures and vector database integrations Knowledge of model selection, fallbacks, prompt versioning, and task decomposition Strong grasp of autonomous agent design, reasoning, tool use, and multi-agent protocols Exposure to near real-time signal/voice processing, filtering, or event-based triggers (e.g., anomaly/event detection) Practical knowledge of statistical modeling, forecasting (ARIMA, Prophet, ML-based), or classical ML pipelines 5+ years in AI/ML engineering, including 1–2+ years working directly on GenAI/Agentic AI projects Preferred Skills Experience with multi-agent collaboration patterns (e.g., A2A, MCP) Deployment experience on GenAI platforms like OpenAI, Claude, Vertex AI, Amazon Bedrock, or Azure GenAI Familiarity with serverless deployment, egress handling, and LLM usage constraints Exposure to evaluation frameworks, benchmarking suites, or internal testing systems Experience integrating AI flows with external systems (BI tools, dashboards, APIs, automation platforms) Prior experience designing multi-modal pipelines combining vision, text, or sensor data Bonus / Good-to-Have Hands-on with fine-tuning workflows (LoRA, PEFT, etc.) Understanding of vector database internals and hybrid retrieval methods Familiarity with CV/multi-modal AI workflows using OpenCV, TorchVision, CLIP, BLIP, or related libraries Prior experience contributing to open-source GenAI libraries or leading internal tool development Why Join Us Help shape the next-gen AI stack from the ground up Work in a fast-moving environment with real ownership and autonomy Collaborate with a cross-disciplinary team at the cutting edge of GenAI and agentic systems Be part of an organization that values experimentation, modularity, and long-term impact Interested candidates can share the CV on shikha.rana@antiersolutions.com Job Type: Full-time Pay: ₹800,000.00 - ₹1,500,000.00 per year Benefits: Paid sick time Provident Fund Schedule: Day shift Monday to Friday Morning shift Application Question(s): How many days of Notice period you have? Experience: Machine learning: 4 years (Preferred) Team management: 4 years (Preferred) Location: Mohali, Punjab (Required) Work Location: In person