1345 Data Scientist Jobs

Key Responsibilities Develop and deploy personalised pricing models using historical behaviour, purchase intent, segmentation, and contextual data. Apply advanced statistical and machine learning techniques to estimate demand curves and user-level price sensitivity. Design and execute pricing A/B tests, analyzing lift, revenue impact, and user experience trade-offs. Develop dynamic pricing frameworks that adjust in real-time based on inputs such as location, time, inventory, and user cohorts. Collaborate with engineering teams to integrate models into pricing engines and user-facing platforms. Communicate findings clearly to business stakeholders and make data-backed pricing recommendations. Must-Have Qualifications 36 years of experience in data science, pricing, or quantitative strategy roles. Strong programming skills in Python and SQL; experience with libraries like scikit-learn, stats models, or XGBoost. Deep knowledge of pricing analytics, revenue management, and behavioral economics. Experience in building predictive models for conversion, elasticity, or revenue uplift. Ability to synthesize complex data into actionable strategies with business impact. Strong experimentation mindset with familiarity in causal inference and A/B testing methodologies.

We are looking for a highly motivated and skilled Data Scientist with 5 years of experience. The ideal candidate should be proficient in Python, ML frameworks, and statistical modeling. You will develop machine learning models, analyze large structured and unstructured datasets, and provide actionable insights to drive business decisions. The role requires strong analytical thinking, effective communication, and hands-on experience with modern data science tools and libraries. You'll work closely with cross-functional teams, enabling data-driven strategies that impact core business outcomes.

JOb Responsibilities: The Data Scientists will leverage expertise in advanced statistical and modelling techniques to design, prototype, and build the next-generation analytics engines and services. They will work closely with the analytics teams and business teams to derive actionable insights, helping the organization in achieving its’ strategic goals. Their work will involve high levels of interaction with the integrated analytics team, including data engineers, translators, and more senior data scientists. Has expertise in implementing complex statistical analyses for data processing, exploration, model building and implementation Lead teams of 2-3 associate data scientists in the use case building and delivery process Can communicate complex technical concepts to both technical and non-technical audience Plays a key role in driving ideation around the modelling process and developing models. Can conceptualize and drive re-iteration and fine tuning of models Contribute to knowledge building and sharing by researching best practices, documenting solutions, and continuously iterating on new ways to solve problems. Mentors junior team members to do the same REQUIRED EDUCATION AND EXPERIENCE: Master’s degree in Computer Science, Statistics, Math, Operations Research, Economics, or a related field Advanced level programming skills in at least 1 coding language (R/Python/Scala) Practical experience of developing advanced statistical and machine learning models At least 2 years of relevant analytics experience Experience in using large database systems preferred Has developed niche expertise in at least one functional domain REQUIRED SKILLS: Ability to work well in agile environments in diverse teams with multiple stakeholders Experience of leading small teams Able to problem solve complex problems and break them down into simpler parts Ability to effectively communicate complex analytical and technical content High energy and passionate individual who can work closely with other team members Strong entrepreneurial drive to test new out of the box techniques Able to prioritize workstreams and adopt an agile approach Willing to adopt an iterative approach; experimental mindset to drive innovation LOCATION: Bangalore What’s in it for you ? Disruptive projects : Work on ‘ breakthrough’ digital-and-analytics projects to enable UPL’s vision of building a future ready organization. It involves deploy ing solutions to help us increase our sales, sustain our profitability, improve our speed to market, supercharge our R&D efforts, and support the way we work internally. Help us ensure we have access to the best business insights that our data analysis can offer us. Cross functional leadership exposure : Work directly under guidance of functional leadership at UPL, on the most critical business problems for the organization (and the industry) today. It will give you exposure to a large cross-functional team (e.g.: spanning manufacturing, procurement, commercial, quality, IT/OT experts), allowing multi-functional learning in D&A deployment Environment fostering professional and personal development : Strengthen professional learning in a highly impact-oriented and meritocratic environment that is focused on delivering disproportionate business value through innovative solutions. It will be supported by on-the-job coaching from experienced domain experts, and continuous feedback from a highly motivated and capable set of peers. Comprehensive training programs for continuous development through UPL's D&A academy will help in accelerating growth opportunities. Come join us in this transformational journey! Let’s collectively Change the game with Digital & Analytics!

Shift: (GMT+05:30) Asia/Kolkata (IST) What do you need for this opportunity? Must have skills required: Machine Learning, NumPy, Data Cleaning, Python, Model evaluation, pandas, Statistics We are seeking a talented Data Scientist II to join our team. The ideal candidate will have 2-5 years of experience in data science and possess expertise in machine learning, deep learning, Python programming, SQL, Amazon Redshift, NLP, and AWS Cloud. ** Duties and Responsibilities: Develop and implement machine learning models to extract insights from large datasets. Utilize deep learning techniques to enhance data analysis and predictive modeling. Write efficient Python code to manipulate and analyze data. - Work with SQL databases to extract and transform data for analysis. Utilize Amazon Redshift for data warehousing and analytics. Apply NLP techniques to extract valuable information from unstructured data. - Utilize AWS Cloud services for data storage, processing, and analysis. Qualifications and Requirements: Bachelor's degree in Computer Science, Statistics, Mathematics, or related field. - 3-5 years of experience in data science or related field. Proficiency in machine learning, deep learning, Python programming, SQL, Amazon Redshift, NLP, and AWS Cloud. Strong analytical and problem-solving skills. - Excellent communication and teamwork abilities. * *Key Competencies - Strong analytical skills. - Problem-solving abilities. - Proficiency in machine learning and deep learning techniques. Excellent programming skills in Python. - Knowledge of SQL and database management. - Familiarity with Amazon Redshift, NLP, and AWS Cloud services. ** Performance Expectations: Develop and deploy advanced machine learning models. Extract valuable insights from complex datasets. Collaborate with cross-functional teams to drive data-driven decision-making. Stay updated on the latest trends and technologies in data science. We are looking for a motivated and skilled Data Scientist I to join our team and contribute to our data-driven initiatives. If you meet the qualifications and are passionate about data science, we encourage you to apply.

About the Role Love deep data? Love discussing solutions instead of problems? Then you could be our next Data Scientist. In a nutshell, your primary responsibility will be enhancing the productivity and utilization of the generated data. Other things you will do are: Work closely with the business stakeholders Transform scattered pieces of information into valuable data Share and present your valuable insights with peers What You Will Do Develop models and run experiments to infer insights from hard data Improve our product usability and identify new growth opportunities Understand reseller preferences to provide them with the most relevant products Designing discount programs to help our resellers sell more Help resellers better recognize end-customer preferences to improve their revenue Use data to identify bottlenecks that will help our suppliers meet their SLA requirements Model seasonal demand to predict key organizational metrics Mentor junior data scientists in the team What You Will Need Bachelor's/Master's degree in computer science (or similar degrees) 2-4 years of experience as a Data Scientist in a fast-paced organization, preferably B2C Familiarity with Neural Networks, Machine Learning, etc. Familiarity with tools like SQL, R, Python, etc. Strong understanding of Statistics and Linear Algebra Strong understanding of hypothesis/model testing and ability to identify common model testing errors Experience designing and running A/B tests and drawing insights from them Proficiency in machine learning algorithms Excellent analytical skills to fetch data from reliable sources to generate accurate insights Experience in tech and product teams is a plus Bonus points for: Experience in working on personalization or other ML problems Familiarity with Big Data tech stacks like Apache Spark, Hadoop, Redshift, etc.

JOb Responsibilities: The Data Scientists will leverage expertise in advanced statistical and modelling techniques to design, prototype, and build the next-generation analytics engines and services. They will work closely with the analytics teams and business teams to derive actionable insights, helping the organization in achieving its’ strategic goals. Their work will involve high levels of interaction with the integrated analytics team, including data engineers, translators, and more senior data scientists. Has expertise in implementing complex statistical analyses for data processing, exploration, model building and implementation Lead teams of 2-3 associate data scientists in the use case building and delivery process Can communicate complex technical concepts to both technical and non-technical audience Plays a key role in driving ideation around the modelling process and developing models. Can conceptualize and drive re-iteration and fine tuning of models Contribute to knowledge building and sharing by researching best practices, documenting solutions, and continuously iterating on new ways to solve problems. Mentors junior team members to do the same REQUIRED EDUCATION AND EXPERIENCE: Master’s degree in Computer Science, Statistics, Math, Operations Research, Economics, or a related field Advanced level programming skills in at least 1 coding language (R/Python/Scala) Practical experience of developing advanced statistical and machine learning models At least 2 years of relevant analytics experience Experience in using large database systems preferred Has developed niche expertise in at least one functional domain REQUIRED SKILLS: Ability to work well in agile environments in diverse teams with multiple stakeholders Experience of leading small teams Able to problem solve complex problems and break them down into simpler parts Ability to effectively communicate complex analytical and technical content High energy and passionate individual who can work closely with other team members Strong entrepreneurial drive to test new out of the box techniques Able to prioritize workstreams and adopt an agile approach Willing to adopt an iterative approach; experimental mindset to drive innovation

Job Title: Senior Data Scientist Intelligent Offer Management Location: Bangalore (3 days work from office) Type: Full-time(On-Site) Experience Level: 3–6 years About the Role: We are hiring a Data Scientist to lead and scale our Intelligent Offer Management engine – a key pillar of our revenue optimisation strategy. This role sits at the intersection of data science, pricing strategy, product thinking, and experimentation, enabling personalised, dynamic, and contextual offer decisions at scale. You'll work closely with product managers, business teams, and engineers to build models that recommend the right offer to the right customer at the right time. Key Responsibilities -Design and implement real-time offer recommendation models using historical purchase, engagement, and behavioural data. - Develop algorithms for dynamic pricing, offer personalisation, segmentation, and discount optimisation. - Collaborate with cross-functional stakeholders to translate business requirements into data science solutions. - Conduct A/B and multivariate experiments to evaluate offer performance and optimise offer strategies. - Build predictive models for conversion propensity, elasticity curves, and uplift measurement. - Package models for production deployment, ensuring scalability, performance, and monitoring. - Influence product design and customer journeys with data-driven insights. Must-Have Qualifications: - 3-6 years of experience in data science, quantitative analytics, or pricing strategy roles. - Proficiency in Python and SQL, with experience in building, validating, and deploying ML models. - Deep understanding of offer science, pricing elasticity, behavioural economics, and incentive systems. - Strong business acumen with the ability to balance accuracy with impact and speed. - Experience working with experimentation frameworks and statistical inference. - Ability to communicate complex insights effectively to both technical and non-technical stakeholders.

Key responsibilities: 1- Development of Predictions Model using ML algorithms in entire customer journey and life cycle (Acquisition, Behaviour, Collections, Propensity, Marketing, Fraud, etc.) 2- Design and Strategy Development to offer best in class Assets Products to customers. 3- Building Prediction Model using big data from digital signals, alternate data and beyond. 4- Partnering, exploring and expanding solutions with different data providers and Companies. 5- Ownership and Responsibility to lead/manage large scale projects/model Development & Deployment. Skill Sets & key requirements: 1- Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 2- Should have worked in Banking/Fintech domain in Credit/Fraud Risk with strong understanding of Bureau Data & in-house banking Data. 3- Advance Coding skills with at least one of these- Python/SQL/Pyspark languages 1- Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. 2- Have worked across end to end in model design, development, validation, documentation & deployment. 3- Create great data stories with expertise in robust EDA and statistical inference.

Key responsibilities: 1- Development of Predictions Model using ML algorithms in entire customer journey and life cycle (Acquisition, Behaviour, Collections, Propensity, Marketing, Fraud, etc.) 2- Design and Strategy Development to offer best in class Assets Products to customers. 3- Building Prediction Model using big data from digital signals, alternate data and beyond. 4- Partnering, exploring and expanding solutions with different data providers and Companies. 5- Ownership and Responsibility to lead/manage large scale projects/model Development & Deployment. Skill Sets & key requirements: 1- Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 2- Should have worked in Banking/Fintech domain in Credit/Fraud Risk with strong understanding of Bureau Data & in-house banking Data. 3- Advance Coding skills with at least one of these- Python/SQL/ Pyspark languages 1- Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. 2- Have worked across end to end in model design, development, validation, documentation & deployment. 3- Create great data stories with expertise in robust EDA and statistical inference.

Role: Data Scientist 2 Key responsibilities: 1- Development of Predictions Model using ML algorithms in entire customer journey and life cycle (Acquisition, Behaviour, Collections, Propensity, Marketing, Fraud, etc.) 2- Design and Strategy Development to offer best in class Assets Products to customers. 3- Building Prediction Model using big data from digital signals, alternate data and beyond. 4- Partnering, exploring and expanding solutions with different data providers and Companies. 5- Ownership and Responsibility to lead/manage large scale projects/model Development & Deployment. Skill Sets & key requirements: 1- Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 2- Should have worked in Banking/Fintech domain in Credit/Fraud Risk with strong understanding of Bureau Data & in-house banking Data. 3- Advance Coding skills with at least one of these- Python/SQL/Pyspark languages 4- Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. 5- Have worked across end to end in model design, development, validation, documentation & deployment. 6- Create great data stories with expertise in robust EDA and statistical inference

Role Synopsis This role is a senior-level position within a data science team, responsible for providing leadership, strategic direction, and technical expertise in the field of data science and machine learning. This role involves leading all aspects of and guiding a team of data scientists while collaborating closely with multi functional departments, such as engineering, product management, and business collaborators. The Lead Data Scientist plays a pivotal role in crafting data-driven strategies and solutions that chip in to the organization's success and growth Key Accountabilities Data Analysis and Modeling: Lead data scientists must have a confirmed foundation in data analysis and a deep understanding of various machine learning algorithms They should be able to apply these techniques to address sophisticated problems and extract valuable insights from data Out of Code computing: Use libraries that support out-of-core computing, such as Dask in Python These libraries can process data that doesn't fit into memory by reading it in smaller portions from disk Business Insight: Understanding the FDO's business objectives and aligning data initiatives with them Project Management: Being skilled in project management methodologies helps in planning and driving data science projects efficiently Machine Learning: Innovation and Strategy - Advanced Machine Learning Skills for complex models. Evaluation Collaboration and Communication: Effective communication with collaborators, Explain the modeling approach and results. Implement to privacy guidelines and recommendation with conscious balance Continuous Learning: Staying up-to-date in competitive edge. Apply methodologies to practical business challenges. Meeting with domain GPO's Data cleaning and preprocessing, analysis: The ability to clean and preprocess data effectively is a fundamental skill in any data science role Data Ethics and Privacy: Open communication with customer. Ethical considerations in algorithm design. Secure data handling, Data minimization Database Management: Proficient in database systems and SQL is required for data retrieval and storage Domain knowledge: Expertise in the domain they are working in to understand the context and requirements of the data projects better Statistical Analysis and Mathematics: Solid grasp of statistical methods and mathematical concepts is needed for data analysis, modeling, and drawing substantial insights from data Experience and Job Requirements Data Science Team plays a crucial role in driving data-informed decision-making and generating actionable insights to support the company's goals. This team is responsible for processing, analyzing, and interpreting large and complex datasets from multiple datasets to provide valuable insights and recommendations across various domains. Through advanced analytical techniques and machine learning models, the data science team helps optimize processes, predict trends, and build data-driven strategies. A bachelor's or equivalent experience or master's degree in quantitative, qualitative field such as Computer Science, Statistics, Mathematics, Physics, Engineering, or a related data field is often required Skills: Leadership role in Data Analysis, Programming proficiency in Python, SQL, Azure Databricks, Statistics & Mathematics. Leadership qualities to steer the team. Strategic direction and technical expertise Soft skills: Active listening, Translate business problems into data questions, Communication and collaboration, Presentation, Problem solving, Multi-functional, Team management, Partner management Data Sources: SAP, Concur, Salesforce, Workday, Excel files Other: Project management. Domain knowledge [Procurement, Finance, Customer], Business Insight, Critical thinking, Story telling Able to prepare analytical reports, presentations and/or visualisation dashboards to communicate findings, important metrics and insights to both technical and non-technical customers Stay up to date with industry trends, standard methodologies and new technologies in data analytics, machine learning, data science techniques.

Job Summary We are looking for an experienced Data Scientist with 3 to 5 years of hands-on experience in building and deploying machine learning and natural language processing (NLP) models. The ideal candidate should have a solid foundation in AI, strong problem-solving abilities, and a passion for applying ML/NLP techniques to solve real-world challenges. Experience in designing AI agents or working with LLMs is a strong advantage. Key Responsibilities Develop, train, and deploy machine learning and NLP models to support business and product goals. Conduct end-to-end model lifecycle management, including feature engineering, model selection, evaluation, and optimization. Perform exploratory data analysis (EDA), hypothesis testing, and derive actionable insights. Design and build AI agents and workflows that interact with LLMs and integrate into larger systems. Collaborate with cross-functional teams including product, engineering, and design to deliver AI-driven solutions. Stay updated with advancements in GenAI, LLMs, and emerging trends in the AI ecosystem. Required Qualifications Bachelors or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field. 3–5 years of experience in applied machine learning, NLP, or AI model development. Proficiency in Python and commonly used data science libraries (e.g., Pandas, NumPy, Scikit-learn, Transformers). Hands-on experience with NLP frameworks (e.g., Hugging Face, spaCy) and model fine-tuning. Strong understanding of supervised/unsupervised learning, vectorization techniques, and evaluation metrics. Excellent problem-solving, communication, and collaboration skills. Preferred Qualifications Experience working with large language models (LLMs) and vector databases. Exposure to building AI agents, autonomous workflows, or prompt engineering. Familiarity with cloud platforms (e.g., AWS) and scalable ML infrastructure. Understanding of MLOps practices for model deployment and monitoring.

Working knowledge of Azure AI Working knowledge of LLM , RAG- Data insights , Reasoning Agent , Chain of thought Knowledge of Graph DB , COSMOS DB , Neo4j Prompt engineering API building Domain knowledge in Finance , data management Should have worked on enterprise AI use cases and one go live solution.

Data Scientist 2-Digital Banking Kotak 811-Regional Sales Role: Data Scientist 2 Key responsibilities: 1- Development of Predictions Model using ML algorithms in entire customer journey and life cycle (Acquisition, Behaviour, Collections, Propensity, Marketing, Fraud, etc.) 2- Design and Strategy Development to offer best in class Assets Products to customers. 3- Building Prediction Model using big data from digital signals, alternate data and beyond. 4- Partnering, exploring and expanding solutions with different data providers and Companies. 5- Ownership and Responsibility to lead/manage large scale projects/model Development & Deployment. Skill Sets & key requirements: 1- Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 2- Should have worked in Banking/Fintech domain in Credit/Fraud Risk with strong understanding of Bureau Data & in-house banking Data. 3- Advance Coding skills with at least one of these- Python/SQL/Pyspark languages 4- Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. 5- Have worked across end to end in model design, development, validation, documentation & deployment. 6- Create great data stories with expertise in robust EDA and statistical inference

Key responsibilities: 1- Development of Predictions Model using ML algorithms in entire customer journey and life cycle (Acquisition, Behaviour, Collections, Propensity, Marketing, Fraud, etc.) 2- Design and Strategy Development to offer best in class Assets Products to customers. 3- Building Prediction Model using big data from digital signals, alternate data and beyond. 4- Partnering, exploring and expanding solutions with different data providers and Companies. 5- Ownership and Responsibility to lead/manage large scale projects/model Development & Deployment. Skill Sets & key requirements: 1- Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 2- Should have worked in Banking/Fintech domain in Credit/Fraud Risk with strong understanding of Bureau Data & in-house banking Data. 3- Advance Coding skills with at least one of these- Python/SQL/Pyspark languages 1- Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. 2- Have worked across end to end in model design, development, validation, documentation & deployment. 3- Create great data stories with expertise in robust EDA and statistical inference

1. Expertise in Data Science & AI/ML: 3-8 years experience designing, developing, and deploying scalable AI/ML solutions for Big Data, with proficiency in Python, SQL, TensorFlow, PyTorch, Scikit-learn, and Big Data ML libraries (e.g., Spark MLlib). 2. Cloud Proficiency: Proven experience with cloud-based Big Data services (GCP preferred, AWS/Azure a plus) for AI/ML model deployment and Big Data pipelines.; understanding of data modeling, warehousing, and ETL in Big Data contexts. 3. Analytical & Communication Skills: Ability to extract actionable insights from large datasets, apply statistical methods, and effectively communicate complex findings to both technical and non-technical audiences (visualization skills a plus). 4. Educational Background: Bachelor's or Master's degree in a quantitative field (Computer Science, Data Science, Engineering, Statistics, Mathematics).

5+ years of DS and AI/ML project implementation experience in one of the cloud environments (AWS/Azure/GCP). Advanced / intermediate level certification is a plus Hands-on experience in data handling using SQL and advanced analytics tools such as Python Hands on Experience in analyzing and visualizing data using tools such as Tableau, PowerBI Experience in scoping, development, validation & deployment of explanatory, predictive, and prescriptive analytic solutions Experience in stakeholder management, driving project governance, and linking analytics to business value delivered Knowledge of Deep Learning models and its applications in Natural Language Processing (NLP), AI/GenAI and Computer Vision(CV) in solving business problems Excellent presentation and communication skills Responsibilities: Participate in client workshops to identify high impact problems that could be solved through AI / ML based solutions Translate business problems (sometimes ambiguous) into clear analytic problem statements. Design a plan to develop and deploy advanced analytics solutions in collaboration with SMEs in Data Science & Engineering. Own & drive execution of the plan by demonstrating techno-functional consulting & stakeholder management capabilities. Measure and articulate how analytic results improve clients business & financial metrics. Perform a role that involves a combination of hands-on contribution, customer engagement and team management. Contribute to organization building through training, mentoring, and developing repeatable analytic approaches

Job Reference # 318000BR Job Type Full Time Your role build advanced statistical models to generate meaningful and actionable insights, improve decision making, optimize the business process, and help address business problems. work on generative ai (genai) models to deliver personalized insights and solutions for clients, financial advisors, and prospects. leverage advanced data science techniques to analyze large datasets and extract actionable insights. collaborate with cross-functional teams to integrate predictive models into wealth management decision-making processes. implement and utilize tools like langchain, langgraph, and vector databases to build scalable, efficient ai-driven solutions. Your team You will be part of the Data Science STAAT Team, WMA Technology. Our Data Science team is at the heart of DAFP’s function to manage and support data science efforts across different business areas. Your expertise bachelor’s degree or higher in machine learning, computer science, computational linguistics, mathematics or other relevant technical field from premier institute. strong foundation in probability, statistics, linear algebra, calculus, machine learning and generative ai/llm. expertise in deep learning techniques and applications. strong understanding of generative ai (genai) including math behind the models and its practical implementations. hands-on experience working with large language model apis like azure openai, anthropic etc.. and cloud ecosystems like azure, aws, gcp, databricks proficiency in langchain, langgraph, and working with vector databases for advanced data retrieval and ai workflows. proficiency in python or similar programming languages, with experience in ai/ml libraries and frameworks such as tensorflow, pytorch, or hugging face. knowledge of financial products, including mutual funds, etfs, and client segmentation, is a plus. About Us UBS is the world’s largest and the only truly global wealth manager. We operate through four business divisions: Global Wealth Management, Personal & Corporate Banking, Asset Management and the Investment Bank. Our global reach and the breadth of our expertise set us apart from our competitors.. We have a presence in all major financial centers in more than 50 countries. How We Hire We may request you to complete one or more assessments during the application process. Learn more Join us At UBS, we know that it's our people, with their diverse skills, experiences and backgrounds, who drive our ongoing success. We’re dedicated to our craft and passionate about putting our people first, with new challenges, a supportive team, opportunities to grow and flexible working options when possible. Our inclusive culture brings out the best in our employees, wherever they are on their career journey. We also recognize that great work is never done alone. That’s why collaboration is at the heart of everything we do. Because together, we’re more than ourselves. We’re committed to disability inclusion and if you need reasonable accommodation/adjustments throughout our recruitment process, you can always contact us. Disclaimer / Policy Statements UBS is an Equal Opportunity Employer. We respect and seek to empower each individual and support the diverse cultures, perspectives, skills and experiences within our workforce.

Greetings From Ushta Te !!! Hiring for the role of Data Scientist for top mnc company - Bangalore !!! JOB DESCRIPTION Mandatory Skills - Data Scientist experience, Python, SQL 12+ years of technical experience in data analytics, data science Responsible for developing and translating computer algorithms into prototype code and maintaining, organizing, and identifying trends in large data sets. Proficiency in SQL database design, proficiency in creating process documentation, strong written and verbal communication skills, and the ability to work independently and on teams. Familiarity with the computer coding languages python, java, kafka, hive, or storm may be required in order to oversee real-time business metric aggregation, data warehousing and querying, schema and data management, and related duties. Should have knowledge of algorithms, data structures, and performance optimism and experience with processing and interpreting data sets. Develop technical solutions to improve access to data and data usage. Understand data needs and advise company on technological resources. Aggregate and analyze various data sets to provide actionable insight. Develop reports, dashboards, and tools for business-users. Key Responsibilities: Lead, mentor, and inspire a high-performing team of Data Scientists, Analysts, and AI/ML Engineers, fostering a collaborative, innovative, and impact-driven environment. Design, develop, and maintain robust data models, analytics frameworks, dashboards, and machine learning solutions that drive business strategy and decision-making. Develop and execute experimentation frameworks, including defining clear success metrics, designing A/B tests, and analyzing results to guide strategic product decisions. Partner closely with product, engineering, and leadership to translate complex clickstream behavioral data into strategic insights that inform product roadmaps and investments. Clearly communicate findings, insights, and recommendations to both technical and non-technical stakeholders, building credibility and driving alignment with senior leadership. Influence product direction by developing hypotheses, employing analytical rigor, and creating clear, impactful presentations and narratives around data-driven insights. Continuously identify new opportunities for leveraging data and analytics to enhance product capabilities, operational efficiency, and customer satisfaction. Drive measurement strategies, set ambitious goals, forecast outcomes, and closely monitor key product and business metrics. If interested kindly share your resume on kausar.rangari@ushtate.co.in/WhatsApp - 7304429460 for further details.

Key responsibilities: 1- Development of Predictions Model using ML algorithms in entire customer journey and life cycle (Acquisition, Behaviour, Collections, Propensity, Marketing, Fraud, etc.) 2- Design and Strategy Development to offer best in class Assets Products to customers. 3- Building Prediction Model using big data from digital signals, alternate data and beyond. 4- Partnering, exploring and expanding solutions with different data providers and Companies. 5- Ownership and Responsibility to lead/manage large scale projects/model Development Deployment. Skill Sets key requirements: 1- Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 2- Should have worked in Banking/Fintech domain in Credit/Fraud Risk with strong understanding of Bureau Data in-house banking Data. 3- Advance Coding skills with at least one of these- Python/SQL/Pyspark languages 1- Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. 2- Have worked across end to end in model design, development, validation, documentation deployment. 3- Create great data stories with expertise in robust EDA and statistical inference

Lead Data Scientist role. Work Location- Hyderabad/ Remote Exp-7+ Years Relevant Notice Period- Immediate Lead Data Scientist Relevant Experience: 7+ Years Job Overview: We are seeking a highly skilled and experienced Senior Data Scientist with expertise in Machine Learning (ML), Natural Language Processing (NLP), Generative AI (GenAI), and Deep Learning (DL). Mandatory Skills • 5+ years of work experience in writing code in Python • Experience in using various Python libraries like Pandas, NumPy • Experience in writing good quality code in Python and code refactoring techniques (e.g., IDE’s – PyCharm, Visual Studio Code; Libraries – Pylint, pycodestyle, pydocstyle, Black) • Strong experience on AI assisted coding experience. • AI assisted coding for existing IDE's like vscode. • Experimented multiple AI assisted tools and done research around it. • Deep understanding of data structures, algorithms, and excellent problem-solving skills • Experience in Python, Exploratory Data Analysis (EDA), Feature Engineering, Data Visualisation • Machine Learning libraries like Scikit-learn, XGBoost • Experience in CV, NLP or Time Series. • Experience in building models for ML tasks (Regression, Classification) • Should have Experience into LLM, LLM Fine Tuning, Chatbot, RAG Pipeline Chatbot, LLM Solution, Multi Modal LLM Solution, GPT, Prompt, Prompt Engineering, Tokens, Context Window, Attention Mecanism, Embeddings • Experience of model training and serving on any of the cloud environments (AWS, GCP, Azure) • Experience in distributed training of models on Nvidia GPU’s • Familiarity in Dockerizing the model and create model end points (Rest or gRPC) • Strong working knowledge of source code control tools such as Git, Bitbucket • Prior experience of designing, developing and maintaining Machine Learning solution through its Life Cycle is highly advantageous • Strong drive to learn and master new technologies and techniques • Strong communication and collaboration skills • Good attitude and self-motivated Mandatory Skills- *Strong Python coding, Machine Learning, Software Engineering, Deep Learning, Generative AI, LLM, AI Assisted coding tools.*Responsibilities: Develop machine learning models using Python & NLP techniques. Optimize data pipelines for efficiency & accuracy. Collaborate with cross-functional teams on product development.

Hiring!! #Data Analysis #ETL #Python #R #SAS # Data Science #Visualization #Automation #Python #Pandas #Scikit-learn #Seaborn #NumPy #Plotly, #SQL #SAS We have urgent opening Data Scientist for at Pune Position: Data Scientist Experience: 3 To 4+ Years Job Location: Pune Key Responsibilities: Translate Business Problems: Apply analytical/technical expertise to convert business challenges into actionable solutions. Project Execution & Automation: Lead and automate analytical projects, ensuring outputs meet client needs and expectations. Data Analysis & ETL: Perform ETL tasks and conduct exploratory, confirmatory, and qualitative data analyses, utilizing statistical models/algorithms in Python, R, SAS, etc. Model Development: Leverage data mining and machine learning techniques to build statistical models across various topics. Database Management: Collaborate with technology and other partners to build and maintain the People Analytics and Insights database. Integrate multiple systems and data sources, transform and clean data, and handle incomplete data sources. Stay Current: Keep up-to-date with current trends and research in data science, research, and technology. Knowledge/Experience: Essential- Machine learning, Statistics, Data engineering Desirable- Tableau Skills: (technical skills) Essential- Data Science, Visualization, and Automation with Python. Python packages like Pandas, Scikit-learn, Seaborn, NumPy, Plotly, etc. Advanced SQL (or similar language for querying relational databases). Desirable SAS, R, and other scripting languages. Tableau and other data visualization tools.

Key responsibilities: 1- Development of Predictions Model using ML algorithms in entire customer journey and life cycle (Acquisition, Behaviour, Collections, Propensity, Marketing, Fraud, etc.) 2- Design and Strategy Development to offer best in class Assets Products to customers. 3- Building Prediction Model using big data from digital signals, alternate data and beyond. 4- Partnering, exploring and expanding solutions with different data providers and Companies. 5- Ownership and Responsibility to lead/manage large scale projects/model Development & Deployment. Skill Sets & key requirements: 1- Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 2- Should have worked in Banking/Fintech domain in Credit/Fraud Risk with strong understanding of Bureau Data & in-house banking Data. 3- Advance Coding skills with at least one of these- Python/SQL/Pyspark languages 1- Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. 2- Have worked across end to end in model design, development, validation, documentation & deployment. 3- Create great data stories with expertise in robust EDA and statistical inference

Functional Area: Data Science & AI/ML Employment Type: Full Time, Permanent Role Category: Engineering and Technology Experience: 4-6 years Job Description: As a growing organization in the healthcare domain, we seek a Data Science Application Expert with expertise in JupyterLab, SAS Studio, and R Studio. The ideal candidate will design, develop, and optimize data science workflows, ensuring robust, scalable, and efficient data processing pipelines. You will collaborate with cross-functional teams to support data-driven decision-making, build machine learning models, and implement analytical solutions aligned with industry standards for security and compliance. Key Responsibilities: Data Science & Application Management: Develop, manage, and optimize JupyterLab, SAS Studio, and R Studio environments for data science workflows. Support and troubleshoot Jupyter Notebooks, R scripts, and SAS programs for performance and reliability. Assist data scientists and analysts in configuring and optimizing their analytical workflows. Maintain and enhance computational environments to support data exploration, statistical modeling, and machine learning. Machine Learning & Analytics: Implement and manage machine learning models using Python (TensorFlow, Scikit-learn, PyTorch), R, and SAS. Automate data preprocessing, feature engineering, and model training workflows. Work with AWS SageMaker, EMR, and other cloud-based ML solutions for scalable model deployment. Monitor and optimize model performance, ensuring reproducibility and efficiency. Data Engineering & Integration: Design and optimize ETL (Extract, Transform, Load) pipelines to handle large-scale healthcare datasets. Integrate JupyterLab, SAS Studio, and R Studio with cloud-based storage (AWS S3, Redshift, Snowflake). Manage structured and unstructured data, ensuring data integrity and compliance with healthcare standards (HIPAA, GDPR). Optimize query performance for large datasets using SQL, Pandas, and SAS data steps. Infrastructure as Code (IaC) & Cloud Deployment: Deploy and manage JupyterHub, RStudio Server, and SAS Viya on AWS, Azure, or on-premise environments. Automate environment provisioning using Terraform, AWS CDK, or CloudFormation. Ensure efficient resource allocation and auto-scaling of cloud-based data science environments. Security & Compliance: Implement role-based access controls (RBAC) for secure access to data science applications. Ensure data security and encryption using AWS KMS, Secrets Manager, and IAM policies. Adhere to HIPAA, GDPR, and other regulatory compliance requirements for healthcare data. Collaboration & Stakeholder Management: Work closely with data scientists, engineers, and business analysts to understand analytical requirements. Participate in Agile ceremonies, including sprint planning, standups, and retrospectives. Provide technical guidance and mentorship to junior team members. Job Requirements: Education: Bachelor’s/Master’s degree in Computer Science, Data Science, Statistics, or related fields. Experience: 4-6 years of experience in data science application management, focusing on JupyterLab, SAS Studio, and R Studio. Technical Skills: Core Data Science Platforms: Expertise in JupyterLab, SAS Studio, and R Studio for data science workflows. Strong understanding of SAS programming (Base SAS, Advanced SAS, SAS Viya), R, and Python. Experience in managing and scaling JupyterHub, RStudio Server, and SAS Viya in cloud or on-prem environments. Programming & Frameworks: Proficiency in Python, R, SAS, SQL, and shell scripting. Experience with Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch for machine learning. Cloud & Infrastructure: Experience in deploying and managing JupyterLab, R Studio, and SAS Viya on AWS, Azure, or GCP. Hands-on experience with AWS SageMaker, Glue, Lambda, Step Functions, and EMR. Proficiency in Terraform, AWS CDK, or CloudFormation for infrastructure automation. Database Management & ETL: Experience with SQL, NoSQL databases (PostgreSQL, DynamoDB, Snowflake, Redshift, MongoDB). Hands-on experience in ETL pipelines and data wrangling using SAS, Python, and SQL. DevOps & CI/CD Tools: Familiarity with CI/CD pipelines using Jenkins, GitLab, or AWS native tools. Experience with Docker, Kubernetes, and containerized deployments. Additional Skills: Event-Driven Architecture: Experience in real-time data processing using Kafka, Kinesis, or SNS/SQS. Security Best Practices: Implementation of secure access controls and data encryption. Cost Optimization: Understanding cloud pricing models and optimizing compute resources. Agile Development: Hands-on experience with Agile methodologies like Scrum and Kanban. Key Attributes: Problem-Solving Mindset: Ability to troubleshoot complex data science workflows and propose scalable solutions. Detail-Oriented: Strong focus on data integrity, performance optimization, and reproducibility. Collaborative: Team player who thrives in a dynamic, cross-functional environment. User-Centric Approach: Commitment to delivering scalable and efficient data science applications.

Who are we? Amdocs helps those who build the future to make it amazing. With our market-leading portfolio of software products and services, we unlock our customers’ innovative potential, empowering them to provide next-generation communication and media experiences for both the individual end user and enterprise customers. Our employees around the globe are here to accelerate service providers’ migration to the cloud, enable them to differentiate in the 5G era, and digitalize and automate their operations. Listed on the NASDAQ Global Select Market, Amdocs had revenue of $5.00 billion in fiscal 2024. For more information, visit www.amdocs.com 📍 Location: Pune 📈 Experience: 6 to 10 Years 🔍 Must-Have Skills: 💡 Generative AI 🐍 Python 📚 RAG (Retrieval-Augmented Generation) 🧠 LLM / NLP 📊 EDA (Exploratory Data Analysis) ☁️ Cloud Platforms (AWS, Azure, GCP) 🔄 Transformers 🧾 Explainable AI 🌌 Deep Learning Desired Background At least 4+ years of relevant experience and track record in Data Science: Machine Learning, Deep Learning and Statistical Data Analysis. MSc or PhD degree in CS or Mathematics, Bioinformatics, Statistics, Engineering, Physics or similar discipline. Strong hands-on experience in Python with the focus being on statistical algorithms development and GenAI practices. Experience with data science libraries such as: sklearn, pandas, numpy, pytorch/tensorflow Experience with GenAI concepts (RAG, LLM) and Agentical development: from conversional to autonomous agents Team player, able to work in collaboration with subject matter experts, with ability to clearly present and communicate findings. Proven ability to build and deliver data solutions in a short time frame. Experience with Azure, docker, and development methodologies - an advantage Proven experiences in productions and DevOPS practices nd find the right answers. Qualifications Bachelor's degree or equivalent experience in quantative field (Statistics, Mathematics, Computer Science, Engineering, etc.) At least 1 - 2 years' of experience in quantitative analytics or data modeling Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms Fluency in a programming language (Python, C,C++, Java, SQL) Familiarity with Big Data frameworks and visualization tools (Cassandra, Hadoop, Spark, Tableau)