Top Data Science Jobs in Mumbai

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.

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.

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%.

The VP, SEO will architect and scale SEO practices, drive organic search innovation, enhance tools, and lead strategic growth for Tier 1 clients, focusing on AI and GEO.

Develop enterprise-scale data science and analytics solutions for Cordant applications. Responsibilities include analyzing complex datasets, applying statistical and AI/ML methods, building scalable models, establishing metrics, ensuring data quality and ethical AI use, collaborating with technical and commercial teams, and communicating insights to stakeholders. The role requires expertise in Python, time series data, LLMs, generative AI, MLOps, and statistical analytics.

Engage prospects and customers to design and implement Data Science and AI solutions across risk, marketing, and collections; lead demos, POCs, proposals, GTM materials, and work with sales and delivery teams to win and implement deals.

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.

Lead regional GenAI product strategy and commercialization across Ascend and Fraud portfolios. Drive discovery, productization, scaling, and adoption of GenAI features; partner with Engineering, Data Science, Sales and GTM; ensure governance, explainability, and measurable commercial outcomes.

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.

Develop and deploy statistical, machine learning, generative AI, and analytics solutions for business needs. Responsibilities include exploratory analysis, predictive and prescriptive modeling, data pipelines, feature engineering, dashboards, model monitoring, MLOps, and scalable cloud-based AI deployments. The role collaborates with engineers, data professionals, and stakeholders, communicates technical findings, measures business impact, and supports responsible AI governance, security, compliance, and continuous innovation.

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.

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.

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.

Lead and scale a Data Science function to deliver production-grade ML solutions that drive customer personalisation, operational optimisation, fraud detection, forecasting and measurable commercial ROI. Own strategy, roadmap, stakeholder engagement, team development, model deployment, monitoring, experimentation, and governance across modern data architectures.

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.

The Director of Data Science will lead the development of AI systems, manage a team, and drive knowledge sharing while ensuring project alignment with business goals.

Lead AI/ML initiatives as an individual contributor: design and implement ML solutions, develop and maintain code, define architecture, guide data infrastructure, and collaborate with cross-functional teams to solve business problems.

The Senior Staff Engineer, Data Science role involves guiding team direction, solving complex challenges for clients, and leveraging technical expertise to innovate solutions.

The Principal Engineer, Data Science role involves leading data science projects and providing strategic insights using extensive IT experience.

Lead AI and data innovation by developing ML models, optimizing data systems, and mentoring junior team members in a collaborative team environment.