Top Data Science Jobs in India

Develop and deploy advanced machine learning, deep learning, graph AI, and generative AI systems for fraud detection, AML, transaction intelligence, and conversational applications. Responsibilities include feature engineering, model evaluation, GPU optimization, scalable batch and real-time deployment, monitoring, and collaboration with technical, business, and academic stakeholders.

Develops production-grade data science solutions using statistical modeling, machine learning, causal inference, and experimental design. Responsibilities include data exploration, feature engineering, model validation, deployment packaging, explainability, model documentation, experiment tracking, and production pipeline collaboration. The role focuses on fraud and risk, predictive maintenance, and patient outcome modeling, while mentoring junior analysts and translating research into platform capabilities.

Design, develop, and deploy enterprise AI/ML solutions including GenAI, LLM-based RAG systems, and agentic AI. Build scalable models, integrations, monitoring, and reusable frameworks while collaborating with stakeholders for production adoption and continuous improvement.

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 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 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.

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.

Develops full-stack AI and machine learning applications using web technologies, statistical modeling, deep learning, databases, cloud platforms, and APIs. Designs algorithms, translates technical specifications into code, performs testing and debugging, documents solutions, and improves product and system performance. Works independently on complex initiatives, collaborates with stakeholders, mentors colleagues, and may manage projects or review others’ work.

Lead end-to-end predictive modeling and experimentation for risk, segmentation, underwriting, and matching. Build, train, deploy, and monitor supervised/unsupervised/forecasting ML models and AI agents. Partner with product, engineering, and operations to embed models in production, run A/B tests and lift analyses, and mentor junior data scientists while ensuring model governance and strong statistical rigor.

Design, develop, and deploy scalable AI/ML and Generative AI solutions (LLMs, RAG, agentic AI). Build AI agents, create intelligent workflows, fine-tune and monitor models, integrate with enterprise systems, and support production deployment and continuous improvement.

The Product Owner is responsible for the product vision, roadmap, and stakeholder alignment in delivering enterprise data products. This includes managing backlogs, ensuring product adoption, establishing governance, and maintaining high-quality standards.

Lead Sigmoid's insurance consulting practice to define and deliver analytics and AI-led transformation across disability, life, supplemental benefits, and absence management. Drive strategy, account growth, cross-functional delivery, solution roadmaps, and thought leadership while partnering with senior client stakeholders to convert business challenges into data-driven solutions and measurable outcomes.

The Data Science Manager will lead a team to drive AI/ML initiatives, develop and deploy scalable ML solutions, and manage projects from ideation to production.

Lead and grow a Data Science team for Truecaller for Business, define strategy and KPIs, architect and deploy scalable ML models, ensure model governance and MLOps best practices, partner with product and engineering, and drive measurable business impact.

The role involves managing SAP BTP projects, ensuring compliance, coordinating with teams, and driving optimization in SAP technology environments.

Design, build, and embed advanced analytic and machine learning models using structured and unstructured data. Translate business needs into modeling solutions, collaborate with application developers for deployment, visualize insights, measure model-driven business performance, and lead model enhancements.

Lead architecture and technical strategy to apply ML, GenAI, and big-data analytics to enterprise storage telemetry. Design and scale ML/DL models and streaming ELT pipelines (Kafka/Spark) for AIOps, predictive failure, capacity forecasting, deduplication, and RAG-driven support. Integrate LLMs, vector DBs, and agentic workflows, collaborate cross-functionally, and mentor senior engineers to productionize analytics across HPE GreenLake hybrid cloud.

The posting provides no specific responsibilities or qualifications beyond the Data Science Manager title. The role appears associated with Accenture’s Data and AI services and resources practice.

Deliver life sciences and pharmaceutical consulting projects using data engineering, data science, statistical modeling, machine learning, and healthcare analytics. Analyze commercial, clinical, real-world evidence, and EMR datasets; develop data-driven recommendations; support strategy, operating models, process design, and transformational change. Collaborate with clients and stakeholders, present insights, contribute to proposals and business development, and create research, methodologies, and thought leadership assets.

Own the company-wide analytics function, defining metrics, reporting, revenue analytics, experimentation, dashboards, and data governance. Build and lead the analytics team while partnering with executives, product, growth, finance, and engineering. Architect the modern data stack, ensure data quality, deliver strategic analysis, and deploy AI-native workflows using Claude, MCP, and agentic tools to automate analytics and reporting.

Conduct applied AI research and develop scalable algorithms combining optimization, ML/deep learning, and knowledge-graph methods. Design, evaluate, and deploy models (transformers, diffusion/generative models) for search and domain-specific applications, collaborate across teams, and communicate results in documentation and publications.

The Analyst will lead analytics for Direct Mail campaigns, driving growth and analyzing data using machine learning techniques while collaborating with various teams.

Validate and govern Finance & Treasury models (CCAR PPNR, URR, macroeconomic models and Non-Model Tools) through methodology review, independent testing, benchmarking, sensitivity analysis, documentation assessment, remediation tracking, and reporting. Partner with stakeholders for regulatory exams, audits, and continuous improvement of validation methodologies and governance.