Top Data Science Jobs in India

Develops data science and Generative AI solutions for prospect targeting and acquisition. Responsibilities include predictive modeling, machine learning, RAG workflows, semantic search, prompt engineering, experimentation, feature engineering, model evaluation, deployment, monitoring, and Responsible AI governance. The role collaborates with product, engineering, and business stakeholders to deliver scalable AI-enabled data products and communicate analytical insights.

Leads data science and application development initiatives, solving complex business and systems problems, defining technical standards, advising on architecture, and developing innovative solutions. Partners with management teams, guides system enhancements, assesses risk and compliance, and coaches developers and analysts. Requires advanced programming, cloud development, systems analysis, project leadership, and cross-team collaboration skills.

Leads a team developing data science, machine learning, deep learning, generative AI, agentic AI, and intelligent automation solutions. The role combines hands-on technical development with project delivery, team supervision, mentoring, resource planning, and priority management. Responsibilities also include IT systems leadership, enterprise application and database oversight, data and information security direction, infrastructure standards, service continuity, personnel evaluation, and operational planning.

Join eBay’s Analytics team to use large-scale customer and marketplace data to identify investment opportunities, evaluate ROI, optimize growth and profitability, and improve buyer and seller experiences. The role operates within the India Analytics Center in Bangalore and collaborates with business units, product managers, and global analytics teams across ecommerce functions.

Analyze buyer and seller behavior, marketplace trends, customer experiences, and business performance using SQL, Python, experimentation, causal inference, segmentation, and funnel analysis. Lead A/B testing, develop measurement frameworks and dashboards, recommend actions, and collaborate with Product, Engineering, Marketing, and Category Management. Design Agentic AI and LLM-based solutions to automate analytical workflows and improve access to insights.

Build scalable data platforms, pipelines, backend services, and self-service analytics tools for eBay’s data teams. Develop notebook environments, SQL editors, workflow orchestration, distributed data processing, and APIs supporting large-scale datasets. Improve platform reliability, scalability, security, and developer experience while using AI coding agents. Collaborate with engineering, analytics, data science, and product teams, contributing to testing, monitoring, documentation, and production-quality software.

Leads supply-chain analytics using statistics, predictive modeling, experimentation, AI, and data-driven storytelling. The role develops analytical frameworks, dashboards, KPI strategies, forecasts, and root-cause analyses while partnering with Product, Operations, Engineering, Customer Experience, and business stakeholders. It translates ambiguous business questions into actionable insights and supports initiatives across shipping, seller experience, customer experience, search, and monetization.

Lead end-to-end product strategy and delivery for AI-driven work intake and platform capabilities. Conduct customer discovery, validate solutions, partner with engineering, UX, AI/ML, and analytics teams, and ship features that improve intake efficiency and experience for admins and end users. Pivot across domains as business priorities evolve and measure business impact for millions of users.

Onboard clients to Blue Yonder’s Luminate Planning solutions and provide applied data science support. Build and optimize machine learning classifiers, conduct data mining, integrate third-party data, cleanse and validate datasets, perform forecasting checks, and present analytical findings. Develop custom AI and ML utilities, address customer data and forecasting concerns, and mentor junior team members. The role requires strong Python-based data science, statistics, SQL, visualization, NLP, neural networks, and supply chain knowledge.

Lead analytics for promotions and incentives strategy, building scalable frameworks to measure promo ROI, automate performance tracking, and apply advanced analytics/ML for segmentation, targeting, and impact measurement. Analyze seller behavior and inventory patterns, partner with cross-functional teams, and translate insights into actionable business strategies to optimize pricing and promotions at scale.

Design, develop, deploy, and maintain production-grade generative AI, agentic AI, and machine learning solutions for aviation operations. Build APIs, data pipelines, predictive models, RAG systems, evaluation frameworks, and MLOps/LLMOps infrastructure using cloud-native technologies, primarily Azure. Ensure security, privacy, observability, reliability, cost efficiency, and responsible AI practices while collaborating with cross-functional teams and mentoring engineers.

Develop and productionize data science, machine learning, optimization, and generative AI solutions for airline and airport operations. Own projects end to end, including use-case prioritization, feature engineering, experimentation, modeling, deployment, monitoring, and MLOps on Azure. Communicate findings to stakeholders, guide junior data scientists, promote reproducibility and responsible AI, and deliver measurable business value.

Lead data science and machine learning projects for pharmaceutical and healthcare clients. Responsibilities include gathering requirements, designing analytical solutions, applying statistics and machine learning, managing project plans and delivery, assuring quality, communicating with stakeholders, and mentoring data science teams. The role also involves using SQL and Python for analysis, reviewing deliverables, monitoring performance, managing risks, and applying pharma-domain knowledge to business challenges.

Deploy and support Snowflake data solutions, resolve production issues, perform data validation and quality checks, configure and maintain tools, and support data governance and compliance. The role also contributes to basic data modeling and dashboard development as needed in a sponsor-dedicated clinical analytics environment.

Leads the architecture and implementation of enterprise GenAI, agentic AI, and machine learning solutions. Translates ambiguous business needs into secure, scalable technical designs; establishes standards for prompt engineering, model evaluation, observability, governance, and responsible AI. Builds reusable AI platforms, reviews designs and code, leads technical discovery and client workshops, performs proofs of concept, and ensures solutions meet functional, security, privacy, compliance, scalability, and performance requirements.

Leads data science strategy and projects supporting sales effectiveness, portfolio performance, forecasting, incentive optimization, risk identification, and growth. Develops predictive, statistical, NLP, machine learning, deep learning, and GenAI solutions, including LLM and RAG applications. Builds scalable data pipelines and models, communicates insights to executives, defines performance metrics, collaborates cross-functionally, and manages a team of data scientists.

Leads growth analytics for Commercial Banking, using customer, transaction, and portfolio data to drive acquisition, portfolio growth, cross-sell, retention, engagement, and sales productivity. Develops segmentation, propensity, churn, next-best-action, campaign, and portfolio models; builds dashboards and KPI frameworks; partners with business and technology teams; and leads analysts and data scientists to deliver measurable outcomes.

Leads the end-to-end workforce management data science lifecycle, including forecasting data ingestion, validation, transformation, modeling, anomaly detection, and dashboard integration. Develops time-series and machine learning models, standardizes WFM metrics and datasets, automates data-quality workflows, and partners with Forecasting, Operations, Finance, IT, and Data Engineering teams to improve forecast accuracy, service levels, efficiency, and decision-making.

Leads the design, development, deployment, and operation of enterprise AI, machine learning, LLM, and agentic AI solutions. Builds production systems across cloud platforms and Databricks, including RAG architectures, vector search, MLOps, LLMOps, model monitoring, and AI guardrails. Provides technical leadership and mentorship while ensuring scalability, security, privacy, reliability, and measurable business outcomes.

Design and develop enterprise generative AI and RAG applications using LLMs, embeddings, vector databases, hybrid search, and agentic workflows. Build ingestion, retrieval, evaluation, observability, and secure API services; optimize accuracy, latency, scalability, and cost. Integrate AI solutions with enterprise systems and collaborate with technical and business stakeholders. Apply prompt engineering, context management, guardrails, security, and responsible AI practices while troubleshooting and improving production applications.

Leads advanced analytics and machine learning initiatives from project scoping through production deployment. Responsibilities include stakeholder management, feature engineering, model evaluation, optimization, production monitoring, drift detection, and scalable model integration. The role requires strong Python skills, statistical modeling expertise, analytical problem-solving, and the ability to communicate technical insights to non-technical stakeholders.

The Senior Data Scientist will develop inventory-yielding solutions using Python, SQL, optimization techniques, and Gurobi. The role may also involve AWS, Git, and Harness. Candidates will apply operations research and optimization methods, including linear, integer, large-scale, and nonlinear programming, within a long-term contract-to-hire engagement.

Develops hybrid AI and simulation models combining machine learning with CFD, FEA, and multiphysics techniques. Designs agentic AI workflows, multi-agent systems, and AI agents integrated with engineering simulation tools. Prototypes, benchmarks, and validates AI solutions for industrial engineering problems while collaborating with simulation, software, and AI teams. Presents technical findings to engineering and leadership audiences.

Teach the Advanced Topics II: Medical Informatics and Telemedicine course in person during the 2027 winter semester. Responsibilities include delivering instruction on data science applications in healthcare, medical diagnosis, data acquisition and preprocessing, modeling, simulation, analysis, data integration, warehousing, mining, and presentation. Candidates must demonstrate subject expertise, hold a relevant PhD, and be licensed or eligible for professional engineering licensure in Canada.

Lead design and delivery of production-grade AI systems including RAG and agentic AI. Develop LLM-based solutions, vector search, embeddings, and autonomous agents. Implement cloud deployments, MLOps (Docker, Kubernetes, MLflow), CI/CD, and workflow orchestration while managing context, memory, tool-calling, and multi-step reasoning.