Top Data Science Jobs in India

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.

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.

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.

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.

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.

Develops agentic AI solutions, statistical models, optimization methods, dashboards, and actionable analytics for supply chain and operations. Builds and validates models using large-scale data, integrates LLM APIs and agentic frameworks, ensures data quality, and translates business problems into analytical solutions. Partners with data scientists, engineers, and stakeholders to deliver production-grade AI products and communicate business implications.

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.

Leads and mentors data scientists while owning end-to-end delivery of advanced analytics solutions. Develops pricing, segmentation, predictive, Bayesian, causal inference, marketing mix, and optimization models. Deploys models through AWS SageMaker, applies explainability techniques, establishes governance and reproducibility practices, reviews technical quality, and communicates insights to clients and senior stakeholders to drive measurable business outcomes.

Leads clinical data science activities for clinical trials, including data management strategy, data integration, analysis, reporting, governance, quality control, and regulatory compliance. Oversees workstreams, ensures timely and high-quality deliverables, provides strategic guidance, collaborates with cross-functional stakeholders, and develops teams. Requires approximately 15% travel.

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.

Develop predictive models, NLP and machine learning systems using large user datasets to improve search, matching, recommendations, and other digital products. Build scalable data products with Engineering and Product teams, applying statistical analysis, vector search, LLM/RAG techniques, embeddings, classifiers, and deep learning. Collaborate across teams to research new methods and deliver production-ready software.

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.

Lead and grow Gracenote's AI & Data Science teams, drive AI/NLP/Computer Vision/MLOps initiatives, deploy production ML/GenAI solutions, enforce quality and lifecycle processes, partner cross-functionally, and advance predictive analytics and research for media audience measurement.

Design and implement production-grade GenAI and agentic AI systems on GCP. Build Dataflow/Beam pipelines to load into a lakehouse, integrate Vertex AI and BigQuery, ensure operational readiness, security, cost optimization, and mentor junior engineers.

Lead end-to-end development and deployment of advanced analytics, ML, and generative AI solutions for commercial stakeholders. Translate business needs into production-grade models and AI applications, build ML pipelines and APIs, apply MLOps/CI-CD, ensure model evaluation/monitoring and responsible AI, mentor teams, and deliver scalable cloud-based data and AI platforms.

Lead a small data science team to research and build NLP/ML solutions (LLMs, RAG, vector search) for search, ranking, and content features; collaborate with product and engineering, estimate and run projects, mentor data scientists, and implement prompt-engineering and agentic workflows to improve user experience.

Lead QA for AI-driven products, designing QA strategy and frameworks (including LLM-as-a-judge), automating test case generation, overseeing model evaluation, coordinating with product and engineering, and managing and mentoring QA teams to ensure high-quality releases.

Lead a data science team to design, develop, and deploy ML/NLP and LLM-based solutions (search, recommendations, content generation/moderation). Convert business requirements into scalable models and pipelines, validate models, collaborate cross-functionally, and recruit and mentor data scientists.

Build predictive models and segmentations to improve direct marketing performance; perform post-campaign analysis and ad-hoc deep dives; translate business problems into analytical solutions and present client-ready findings.

Design, build and validate credit risk scorecards (application, behavioral, collections). Extract, clean, and preprocess data using SQL; develop ML models in Python. Monitor model performance, backtest and recalibrate to meet regulatory standards. Collaborate with business teams and document modeling processes for governance and audit.

Design, build, and validate statistical and machine learning models. Clean and transform large datasets, engineer features, evaluate and fine-tune models, and present analytics-driven insights to clients while managing deliverables and translating business requirements into analytical solutions.