Top Data Science Jobs in India

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 Content Intelligence and AI-enabled structured data capabilities: design models and architectures, govern content lifecycle and taxonomy, integrate AI-driven metadata enrichment, align product roadmaps, build QA/governance, and mentor cross-functional teams to scale data science and content workflows.

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.

Lead the development of AI models for medical imaging, working with cross-functional teams to implement cutting-edge computer vision techniques and ensure compliance with regulatory standards.

Lead regional GenAI product strategy and commercialization across Ascend and Fraud. Own roadmap, revenue, and adoption; drive discovery, productization, scaling, GTM, governance, and cross-functional delivery to embed explainable GenAI into core platforms and client solutions.

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.

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.

This role involves developing AI and ML solutions to enhance decision-making and operational efficiency in finance and HR, leveraging data insights for strategic analysis and predictive modeling.

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.

Lead the product management for data & analytics solutions, coordinating between business stakeholders and IT to deliver scalable, high-value solutions and ensuring ongoing enhancement and user adoption.

The AI Recruiter will manage the entire recruitment process for AI-related roles, collaborate with stakeholders on hiring strategies, and maintain a talent pipeline.

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.

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, 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.

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