Top Data Science Jobs in India

Lead the architecture, development, and deployment of AI and optimization systems for travel platforms. Integrate mathematical algorithms, machine learning, LLMs, generative AI, and MLOps into production systems using Python and C++. Drive research innovation, modernize legacy workflows on GCP, establish AI lifecycle standards, collaborate on technical roadmaps, influence AI adoption, and mentor junior data scientists and engineers.

Leads data science and machine learning architecture engagements, analyzing diverse datasets and deploying models using cloud and big data technologies. Designs scalable, cost-effective solutions, including generative AI applications, and provides technical leadership across client delivery and presales activities. Creates standards, accelerators, reports, and presentations; mentors junior staff; and communicates technical concepts to business stakeholders.

The Financial Services Leader will oversee the FS Consulting Practice, managing high-level strategy, client relationships, and operational delivery across several accounts while fostering innovation and cultural transformation.

The Engagement Manager will lead client workshops, drive analytics roadmaps, and liaise with clients to provide solutions in life sciences consulting.

The role requires applying data science principles to solve complex problems, contributing to the development and deployment of data science solutions, and provides insight based on data analysis.

The Data Scientist will analyze and model data for ML systems, focusing on Personalization and Recommendation Engines, and collaborate with teams to enhance data-driven solutions.

Lead architecture and delivery of enterprise AI/ML systems (LLMs, RAG, agentic systems). Provide hands-on technical leadership, design production pipelines, model serving, evaluation frameworks, and governance. Collaborate with data engineering, software, IT, and business teams to ensure secure, explainable, and well-governed AI solutions from prototype through production.

Leads GenAI, machine learning, data science, and modeling initiatives for pharmaceutical product development. Partners with scientific leaders to identify high-impact AI opportunities, develops scalable AI products, influences modeling strategy and investment, establishes AI-enabled decision-making practices, and mentors data scientists. The role requires strong problem framing, stakeholder influence, technical expertise across advanced machine learning and generative AI, and the ability to translate emerging technologies into measurable business outcomes.

Lead oncology marketing analytics for global oncology portfolio and biomarker strategy. Deliver hands-on analytics and simulation modeling using Python, SQL, Power BI and GenAI; generate actionable insights from real-world oncology data and communicate recommendations to cross-functional stakeholders to inform launch and commercialization.

Lead architecture and delivery of enterprise AI/ML systems and applications (LLMs, RAG, agents). Provide technical leadership, set engineering standards, oversee model lifecycle, ensure governance/security, collaborate with data and software teams, and drive production deployment and operational support while mentoring a multi-person AI engineering team.

Lead data science initiatives for Quvia’s connectivity platform by developing predictive, prescriptive, forecasting, classification, and optimization models. Integrate anomaly detection, knowledge graphs, neural networks, and generative AI into products; deploy and continuously refine production-ready models; establish data quality and governance standards; document workflows; and mentor junior data scientists while collaborating cross-functionally with engineering and product teams.

Teach and develop intermediate-to-advanced courses in AI, ML, and Data Science using hands-on projects. Create syllabi and course materials, mentor students on projects, update curriculum with emerging tools (TensorFlow, Spark, cloud AI), participate in committees, recruitment, and professional development.

Lead end-to-end delivery of large-scale data engineering and AI/GenAI programs. Define scope, timelines, and release plans; manage multi-workstream teams, risks, stakeholders, and production readiness. Mentor and grow engineering teams, run architecture reviews, and ensure solutions meet performance, cost, observability, and governance requirements.

As an Associate Product Manager, you will manage product development cycles, collaborate with engineering and operational teams, evaluate partnerships, and prioritize projects to improve customer adoption.

The Engagement Manager will drive execution of business-analytics roadmaps, lead workshops, define business problems, and manage client relations. This role combines hands-on contributions with team and customer management.

Build and govern LookML semantic layers, Explores, derived tables, and metrics for dashboards and conversational analytics. Develop high-performance SQL and BigQuery data models, validate data, and create Looker dashboards. Connect LLM-based query tools and AI assistants to governed data sources, contribute to agentic analytics workflows, and collaborate with business stakeholders on consumer behavior, churn, and revenue analysis.

Develop and deploy machine learning, deep learning, graph AI, and generative AI systems for fraud detection, AML, transaction intelligence, and conversational AI. Responsibilities include feature engineering, model evaluation, GPU optimization, production deployment, scalable pipeline development, model monitoring, and collaboration with academic and cross-functional teams. The role involves LLMs, RAG, agentic workflows, graph neural networks, federated learning, and real-time financial systems.

Develop end-to-end AI systems for fraud detection, AML, anomaly detection, transaction intelligence, graph analytics, and conversational AI. Responsibilities include feature engineering, ML and deep learning model development, LLM and agentic applications, GPU optimization, scalable batch and real-time deployment, evaluation, monitoring, and collaboration with technical, academic, and business stakeholders.

Translate business problems into data science solutions; analyze and clean large datasets; engineer features; build, evaluate, optimize, deploy, and monitor machine learning and statistical models. Collaborate with engineering teams to productionize models and pipelines, communicate findings to nontechnical stakeholders, retrain models as needed, and document methodologies and results.

Develop and deploy 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 production pipeline development. The role also involves LLM and agentic workflows, RAG, graph-based modeling, privacy-preserving AI, and collaboration with academic, technical, product, and business teams.

Design and deliver end-to-end, production-grade AI and machine learning systems, including data engineering, feature design, model deployment, scaling, monitoring, and observability. The role combines hands-on data science expertise with technical leadership, covering deep learning, big data systems, CI/CD, Kubernetes, GPU computing, federated AI, LLMs, and agentic AI.

Lead the design and delivery of large-scale, production-grade AI and machine learning systems. Responsibilities span data engineering, feature design, model development, deployment, scaling, monitoring, and observability. The role requires hands-on expertise with machine learning, deep learning, cloud-native infrastructure, GPU computing, CI/CD, distributed data systems, federated AI, LLMs, and agentic AI, along with leadership and system design focused on latency and scalability.

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.

Lead design and delivery of production-grade AI systems including RAG and agentic AI. Develop LLM-based solutions, vector search, embeddings, and autonomous agents. Implement cloud deployments, MLOps (Docker, Kubernetes, MLflow), CI/CD, and workflow orchestration while managing context, memory, tool-calling, and multi-step reasoning.

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.