Top Data Science Jobs in Mumbai

Leads reporting analytics and modeling initiatives that deliver business insights to leadership. Designs dashboards, produces forecasts and reports, identifies risks, and improves performance through stakeholder engagement. Manages analytics projects and teams while ensuring data accuracy and quality. Requires advanced Excel, Power BI or Tableau, SQL, VBA, predictive analytics, data science, automation, storytelling, project management, and problem-solving skills.

Analyzes air cargo industry trends, business processes, and operational needs to identify solution opportunities. Translates requirements into functional specifications and user stories, facilitates stakeholder workshops, validates solution designs, and supports proofs of concept. Collaborates with sales, architects, product managers, and delivery teams on proposals, RFPs, business cases, pricing, ROI, and market positioning. Provides air cargo domain advisory across freight forwarding, customs, handling, and last-mile logistics.

Seeking senior software engineers with proven expertise in at least one technical area, including AI/ML, LLMs, data, full-stack or backend development, automation, cloud infrastructure, and DevOps. Responsibilities include solving complex problems independently, collaborating across technologies, and delivering production-quality projects in a startup environment. Engagements may be project-based or ongoing.

Develop MSME commercial credit-risk models, scorecards, portfolio analytics, and predictive features using bureau data, Python, and SQL. Build reproducible datasets, validate model performance and stability, conduct portfolio diagnostics, and create client proofs of concept. Support pre-sales consulting, analytical solution design, stakeholder presentations, model governance, documentation, and productization of reusable analytics.

Develop data-driven risk and analytics products using consumer bureau and financial-services data. Responsibilities include data preparation, exploratory analysis, feature engineering, statistical and machine-learning model development, validation, documentation, reusable coding, UAT support, and productisation. The role partners with Product and Technology teams to enhance analytics capabilities, assess performance, maintain governance standards, and communicate analytical findings to technical, business, and client stakeholders.

Build, train, evaluate, and deploy machine learning and generative AI models for fraud detection, financial agents, and safety systems. Develop high-throughput data and inference pipelines using distributed technologies, optimize GPU model serving, maintain feature stores, fine-tune language models, and implement evaluation guardrails. Collaborate with architects and engineers while applying MLOps, cloud, containerization, and software engineering practices.

Own Tala’s product strategy and roadmap for fraud prevention, authentication, identity, and risk capabilities. Partner with Engineering, Data Science, Analytics, and business teams to build data-driven fraud products, define decisioning metrics and thresholds, manage fraud incidents, improve customer security, and minimize friction. Lead cross-functional execution from discovery through launch and iteration while managing external technology partners.

Develop data-driven analytics products using consumer bureau and financial-services data. Responsibilities include data preparation, exploratory analysis, feature engineering, statistical and machine-learning modeling, validation, reusable code development, UAT support, productisation, documentation, and collaboration with Product and Technology teams. The role also requires adherence to data security, model governance, compliance, and quality standards.

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.

Lead end-to-end MSME credit-risk analytics, including scorecard and predictive-model development, bureau feature engineering, portfolio monitoring, early-warning analysis, validation, and proofs of concept. Translate analytical findings into client-ready lending recommendations, support pre-sales engagements, review code and models, mentor analysts, and collaborate on product implementation. Ensure solutions are interpretable, stable, well-documented, compliant, and suitable for production monitoring.

Analyze large bureau and client datasets to identify trends, risks, segments, and growth opportunities. Build statistical models, benchmarks, analytical datasets, and automated workflows using Python and SQL. Translate client questions into analytical approaches, validate findings, and communicate insights through charts, reports, and presentations. Maintain documentation, reproducibility, data quality, governance, and peer-review standards while supporting market intelligence and client-facing deliverables.

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.

Leads a team of scientific data analysts supporting Reaxys, Embase, and future AI-enabled life sciences platforms. Oversees search and API quality, data discoverability, testing, feature releases, quality standards, and customer-focused insights. Uses scientific expertise, Python and Bash scripting, databases, cloud platforms, and AI-enabled testing workflows to improve data integrity and platform performance. Coordinates stakeholders, documents work, presents findings, and drives cross-functional projects.

Leads a team of scientific data analysts supporting Reaxys, Embase, and future AI-enabled life sciences platforms. Oversees search and API quality evaluation, data discoverability, testing, feature releases, customer-focused insights, and quality standards. Uses scientific expertise, data analysis, scripting, databases, cloud platforms, and AI-enabled testing workflows to improve platform reliability and relevance. Coordinates stakeholders, advances projects, documents work, and presents recommendations for product and data improvements.

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.

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.