Top Data Science Jobs in Mumbai

Leads Pfizer’s India Integrated Insights & Strategy function, advising commercial executives through analytics, market research, forecasting, and data-driven brand and portfolio strategy. Owns the analytics roadmap, budget, performance measurement, PMR strategy, vendor partnerships, and resource allocation. Oversees advanced analytics, AI/ML, customer segmentation, targeting, field-force sizing, and launch planning while developing a high-performing team and coordinating with regional and global GCA stakeholders.

Supports project management within a government-embedded agriculture PMU, coordinating stakeholders, implementing activities, and managing program monitoring and learning. Responsibilities include developing surveys and focus group instruments, conducting research, preparing briefs and presentations, maintaining documentation, supporting budgeting and hiring, organizing events, and disseminating findings through reports, blogs, and policy briefs. The role requires strong economics and field research knowledge, excellent communication and organizational skills, creative problem-solving, and willingness to travel across Maharashtra.

Leads the AI and Automation Activation team as a people manager and player-coach. Partners with business and technology stakeholders to identify, activate, and scale agentic AI, generative AI, analytics, and automation use cases. Drives adoption, roadmap development, measurable value realization, responsible AI governance, monitoring, and continuous improvement while coaching team members and developing activation playbooks and operating practices.

Lead end-to-end MSME credit-risk analytics, including scorecard and predictive model development, bureau feature engineering, portfolio monitoring, validation, early-warning analysis, and proofs of concept. Translate analytical results into client-ready recommendations for banks, NBFCs, fintechs, and lenders. Own pre-sales solutioning, review analytical work, mentor analysts, and ensure strong governance, documentation, reproducibility, and implementation readiness.

Support Private Bank operating model and process excellence initiatives by analyzing data, identifying automation and AI opportunities, developing business cases, and delivering workflow, reporting, and decision-support solutions. The role coordinates business, operations, technology, and COO stakeholders; manages requirements, governance, testing, implementation, adoption, and continuous improvement while using analytics to reduce risk, improve productivity, and enhance client outcomes.

Drives growth through complex, high-value sales opportunities across data, analytics, AI, and SaaS solutions. Owns the full sales cycle, expands executive stakeholder networks, supports strategic renewals, leads workshops, and communicates measurable customer value. Partners with account managers and cross-functional teams in enterprise, academic, and government environments. Extensive regional travel and engagement with senior stakeholders are required.

Drives strategic sales growth across data, analytics, AI, and SaaS solutions for academic, government, and enterprise accounts. Owns complex opportunities through the full sales cycle, expands executive stakeholder networks, supports high-value renewals, leads workshops, and communicates measurable customer value. The role requires strong commercial judgment, technical fluency, senior-level presentation skills, cross-functional collaboration, and extensive regional travel.

Drive full-cycle sales for complex, high-value AI research workspace opportunities. Originate and close new business, support strategic renewals, expand stakeholder relationships, lead value-based workshops, and translate AI and research analytics capabilities into measurable customer outcomes. Engage senior decision-makers across enterprise, government, academic, and research organizations while developing pipelines, territories, business cases, negotiations, and long-term account growth.

Owns the full sales cycle for complex, high-value AI research workspace opportunities, from prospecting and discovery through negotiation and closure. Builds executive stakeholder relationships, generates new business, supports strategic renewals, leads workshops, develops business cases, and communicates measurable value from AI-powered research and knowledge workflow solutions. Requires enterprise sales expertise, AI fluency, consultative selling skills, and extensive regional travel.

Data Science Interns use machine learning, statistics, experiments, algorithms, and data exploration to solve business and product problems. They formulate analytical approaches, interpret and validate results, collaborate with stakeholders, produce actionable insights, and help improve Microsoft products and customer experiences through peer review and iterative analysis.

Drive enterprise sales growth across India by generating new business and expanding strategic accounts. Sell complex HR, L&D, assessment, and analytics solutions to C-suite buyers, advise on workforce transformation, collaborate cross-functionally, forecast using data, and represent the company at industry events. Manage long, multi-stakeholder deals and travel up to 40%.

Build and maintain scalable analytics infrastructure and predictive models; define analytics roadmap; run experiments for acquisition, churn, delinquency and profitability; create dashboards and automated reports; monitor model and business performance; ensure data accuracy and partner with cross-functional teams.

Leads the organization’s data science and AI roadmap, managing a hands-on team that develops statistical models, experiments, forecasting, segmentation, production AI solutions, and agentic workflows. Drives GCP-based model deployment, MLOps/LLMOps, governance, observability, ethical AI, and adoption of AI tools. Partners with business, engineering, product, BI, and analytics teams to deliver practical data products, define success metrics, and communicate technical insights to executives.

Evaluate AI-generated technical content, craft and answer advanced ML/AI prompts, assess model responses for accuracy, relevance, and clarity, identify errors or hallucinations, and provide feedback to improve AI systems.

Support cross-product analytical validation from a quantitative finance perspective, identify quality improvements, and promote modular architecture and reuse. Enable AI-driven workflows by defining best practices, evaluating use cases against governance standards, and transitioning manual processes to automation. Collaborate with product, engineering, research, and platform teams to apply consistent quality and architecture standards across business lines.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.

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.

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/CUDA optimization, RAG and LLM development, federated AI exploration, production deployment, scalable pipeline design, and monitoring. The role involves working with transactional data, graph models, real-time systems, and cross-functional research and industry collaborators.

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.

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 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.

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.

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 role involves analyzing data to drive strategic decisions and improve employee benefits services. Requires strong analytical skills and experience in data science.

Lead advanced marketing analytics and measurement initiatives, build attribution, MMM and predictive models using structured and unstructured data, manage vendor relationships, present insights to senior stakeholders, and drive media test-and-learn and measurement roadmap execution across acquisition, engagement, and retention.