Top Data Science Jobs in Mumbai

Lead end-to-end AI architecture and delivery for commercial initiatives: design RAG and agent systems, select/configure LLMs, build data and MLOps pipelines, write production-quality code, mentor engineers, partner with business stakeholders, and champion responsible AI practices to scale commercial AI solutions.

Lead design, development, and deployment of marketing mix modeling, multi-touch attribution, and agentic AI solutions to quantify marketing ROI, optimize budgets, mentor analytics teams, and translate insights for commercial stakeholders.

Hands-on individual contributor who designs, builds, and deploys production-grade generative AI, RAG, and agentic systems for commercial analytics. Responsibilities include end-to-end RAG and agent pipelines, vector DB and embedding engineering, cloud LLM integrations, data pipelines, MLOps, model monitoring, prompt engineering, and cross-functional collaboration with compliance and business stakeholders.

Design, develop, and maintain production ML solutions for trade surveillance and financial crime. Perform end-to-end model lifecycle work (feature engineering, training, validation, calibration, monitoring), support model governance and audits, collaborate with SMEs and MLOps for CI/CD and productionization, deliver interpretable explainability outputs, and incorporate GenAI/LLMs for unstructured text signal extraction.

Design, develop, and maintain production ML solutions for trade surveillance and financial crime use cases. Apply supervised and unsupervised methods, execute model lifecycle (feature engineering, training, validation, monitoring), support model governance, partner with MLOps for CI/CD, deliver explainable outputs, and leverage GenAI/LLMs for unstructured data extraction and summarization.

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.

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.

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.

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.

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.

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.

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.

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

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.

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

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

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

Lead post-deployment operations and optimization of Agentic AI, LLM integrations, and Python-based analytical pipelines. Supervise a data science operations team, ensure secure high-availability production systems, manage integrations with enterprise systems (SAP, Salesforce, Postgres), and drive continuous improvement, monitoring, and adoption of analytics solutions.

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.

The Facilitator will deliver academic support in Data Analytics through live sessions, tutoring, and quality assurance, while collaborating on curriculum enhancements and mentoring others.

Lead data science initiatives using advanced statistical techniques and machine learning to produce actionable insights. Partner with cross-functional teams to design data-driven solutions, manage and ensure integrity of large datasets, develop predictive models to forecast trends, and communicate results to stakeholders while staying current on emerging technologies.