Top Data Science Jobs in India

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.

The AI Capability Lead will define the AI strategy, architect enterprise AI solutions, and mentor teams while advising clients on AI implementation.

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.

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.

Design, build and embed advanced analytic and machine learning models into large-scale business processes. Translate business requirements into mathematical models, collaborate with application developers for deployment, create visualizations and communicate insights to stakeholders, and develop best practices and methodologies.

Looking for a data scientist to build industry-specific digital solutions using machine learning and optimization algorithms for industrial analytics. Responsibilities include developing models, analyzing data, and creating prototypes.

The Senior Manager, Data Science will lead hands-on analytics and algorithm development using wearable data. Responsibilities include building Python pipelines, developing models, performing statistical analysis, and collaborating with teams.

Own and operate data streaming platforms centered on Confluent Kafka. Manage Confluent Cloud and self‑managed Kafka on Kubernetes, implement supporting components (Schema Registry, Kafka Connect, Flink), build IaC and CI/CD automation, set up monitoring and alerting, troubleshoot production incidents, perform capacity planning, and partner with application, security, and cloud teams to enable event‑driven architectures and developer self‑service.

Lead and grow the Shipping Analytics team to define metrics, drive experimentation, build AI-enabled analytics (including LLMs), deliver insights that influence product and business decisions, and scale analytics infrastructure and self-serve tooling.

Lead architecture, development, deployment, and scaling of LLM, NLP, and Computer Vision solutions. Build RAG, prompt engineering, fine-tuning, multimodal AI, and production ML pipelines. Mentor data scientists, drive AI strategy, ensure model governance, and collaborate with product, engineering, and business stakeholders to deliver enterprise AI systems.

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.

The role involves leading data science initiatives, mentoring team members, deploying models, and ensuring high-quality technical solutions. Responsibilities include coding, architecture design, and client requirement translation.

Lead and grow Gracenote's AI/ML and Data Science teams, establish technical rigor and operating cadence, manage cross-functional stakeholder delivery, oversee ML/GenAI/NLP/computer vision research and production deployments, drive MLOps and QA, automate recurring analyses, and evolve predictive analytics and market-research methodologies for media measurement.

The Senior Manager will lead the Data Science and Generative AI team, defining the AI strategy and overseeing the development of AI solutions while mentoring staff and ensuring best practices in AI governance.

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

Lead and grow a Data Science & AI team, drive AI initiatives and POCs, collaborate with stakeholders, manage external platform relationships, ensure responsible AI alignment, and deliver predictive analytics and AI-enabled solutions for internal and client use.

The role involves developing and implementing AI/ML solutions for clinical trials, predictive modeling, and compliance with regulations. Requires strong programming skills and experience in generative AI.

The role involves designing, building, and operating analytics pipelines on Google Cloud Platform, utilizing modern practices for orchestration, transformation, and machine learning integration.

The Facilitator will deliver academic support in Data Analytics through live sessions, tutoring, and quality assurance, while collaborating on curriculum enhancements and mentoring others.

The QA Data Science Engineer will establish QA best practices for AI-driven security products, perform testing on data pipelines, and integrate ML tests into CI/CD pipelines.

As a Clinical Data Science Programmer, you will develop and validate programming code for clinical trials, ensuring data integrity and compliance with regulatory standards while collaborating with scientists and statisticians.

Design, develop, and deploy generative AI and ML/DL solutions (including RAG, conversational and multi-agent systems) for fraud operations. Build POCs and production models, implement AI pipelines, optimize performance, ensure data quality, follow MRM and Fair Lending guidelines, collaborate cross-functionally, and mentor junior staff.

Lead a team of data scientists to apply data mining techniques, conduct statistical analysis, and build predictive systems for technology intelligence.

Lead the Data Science team for Lending at Grab, overseeing credit risk modeling, guiding ML strategies, and partnering with stakeholders for data-driven insights and model governance.