Top Data Science Jobs in India

As a Staff Data Scientist I, you'll design, implement, and operate a Machine Learning platform, collaborating with teams to enhance existing systems and processes, ensure reliability, and apply software engineering patterns for model serving.

Apply statistical and machine learning methods to marketing datasets to build segmentation, propensity, attribution, LTV, and personalization models. Clean and structure client data, translate business questions into analytical approaches, present findings to stakeholders, and collaborate to operationalize models into marketing platforms and production workflows.

Lead design, development, and operationalization of AI solutions (LLMs, GenAI, RAG, NLP, agent-based) to automate finance workflows. Translate business problems into technical approaches, partner with global finance and engineering teams, ensure responsible AI governance, drive program delivery, stakeholder engagement, change management, and measurable outcomes.

The Principal-Global Feasibility Data Science role involves managing complex feasibility assessments independently and collaborating with various stakeholders to derive innovative solutions.

Partner with product owners and stakeholders to design and deliver descriptive, predictive, and AI-enabled analytics for provider domains. Build datasets, implement models and RAG/agent workflows, ensure data and AI governance compliance (EU AI Act), collaborate with engineering and governance teams, and track business impact of analytics and data products.

Designs and builds production-grade ML/AI platforms, pipelines, and LLM applications. Implements CI/CD, containerized deployments, MLOps practices, monitoring, and RAG/agentic systems. Collaborates with data scientists and stakeholders to translate requirements into scalable solutions and drives model lifecycle operations including monitoring, retraining, and decommissioning.

Lead design and integration of AI-powered features and agentic workflows into a cloud-native quoting platform. Architect scalable GCP/AWS solutions, use LLMs and AI assistants, enforce CI/CD and testing standards, mentor engineers, and drive responsible AI practices across the team.

Lead technical architecture and AI strategy for a cloud-native quoting platform. Drive high-impact engineering initiatives, integrate LLMs and agentic AI, enforce AI governance and engineering standards, mentor senior engineers, and translate product strategy into a technical roadmap.

The Data Science Engineer will analyze and prepare raw data, develop data models, and proficiently use SQL and Python for ETL processes.

Lead data science initiatives, develop and evaluate models, translate business problems into actionable insights, and mentor junior analysts. Collaborate with teams to operationalize AI-driven insights for measurable business impact.

Manage and mentor a team of data analysts, design and deploy advanced analytics models, and engage with clients to translate business challenges into analytics solutions.

Design, build, and productionize end-to-end generative AI and ML pipelines, including retrieval/embedding services, agentic systems, model training/fine-tuning, evaluation, monitoring, and cloud-deployed APIs/microservices in collaboration with engineering and product.

Lead and manage a team of Data Scientists and Data Analysts, establishing data standards and practices in Stripe's India office while driving data-centric decision-making across the organization.

Lead revenue analytics for Asset Servicing focused on client profitability, price benchmarking, fee schedule and invoice simplification. Manage and hire analytics staff, apply data science and consulting approaches to optimize pricing and invoicing in a securities/financial services context.

Build and maintain scalable analytics infrastructure and predictive models; define analytics roadmap; run experiments for acquisition, churn, delinquency and profitability; create dashboards and automated reports; monitor model and business performance; ensure data accuracy and partner with cross-functional teams.

Lead design and delivery of NLP and Generative AI solutions, from problem definition through deployment. Build and fine-tune transformers and LLMs for summarization, QA, classification and chatbots, apply classical and deep learning methods, ensure scalable reproducible ML workflows in cloud environments, mentor junior data scientists, and collaborate with product and engineering to translate business problems into AI solutions.

As Manager of Engineering, you will lead teams in building AI-driven, cloud-based applications, oversee product delivery, and drive technical strategy while fostering team growth and operational excellence.

Lead development and deployment of marketing measurement models (MMM, incrementality, forecasting) using Bayesian methods; partner with AI agents and engineering to operationalize agentic analytics, deliver client-ready insights, train teams, and mentor junior data scientists.

Lead development and deployment of marketing measurement models (MMM, incrementality, forecasting) using Bayesian methods. Collaborate with AI Agents and Data Engineering to build scalable, client-facing solutions, validate tools, train teams, produce client deliverables, and mentor junior data scientists.

Lead development and deployment of marketing measurement models (MMM, incrementality, forecasting) using Bayesian methods. Partner with AI Agents and Data Engineering to productionize agentic analytics, validate deployed tools, train teams, mentor juniors, and produce client-ready insights and GTM recommendations.

Lead development and deployment of marketing measurement models (MMM, incrementality, forecasting) using Bayesian methods; partner with AI Agents and Data Engineering to productionize models, create client deliverables, train teams, mentor junior scientists, and establish scalable methodology.

Lead design and development of distributed microservices using Java and Spring Boot; architect scalable RESTful APIs; drive cloud-native solutions on GCP; provide technical leadership and mentoring; own application lifecycle, performance tuning, incident resolution, and CI/CD/cloud automation.

Lead design, development, and deployment of multi-agent, agentic AI solutions using LangGraph, LLM orchestration, RAG, and RL-based fine-tuning. Build scalable architectures, integrate vector databases and knowledge graphs, run PoCs to demonstrate business impact, and mentor AI engineers and data scientists for production-grade autonomous agents.

Lead the architecture and implementation of agentic AI systems: multi-agent orchestration, real-time integrations, STT/NLU ingestion, LLM fine-tuning and RAG pipelines, governance and human-in-the-loop controls, and scalable, low-latency platform design to enable proactive incident triage and automated investigations.

Lead research, design, and deployment of multi-agent, agentic AI systems using LangGraph and LLM orchestration. Build memory-augmented agents, implement RAG with vector DBs, apply RLHF/RLAIF, prototype PoCs, scale production architectures, and mentor AI engineers.