Top Data Science Jobs in Mumbai

Leads engineering and data science teams building AI/ML data collection systems for large-scale unstructured data. Provides technical direction across NLP, classifiers, LLMs, MLOps, data pipelines, cloud platforms, security, and system operationalization. Responsibilities include mentoring and hiring talent, overseeing scalable model and pipeline development, collaborating cross-functionally, improving delivery processes, and ensuring reliable, secure, high-performance solutions.

Leads the design, development, and deployment of production-ready AI and machine learning solutions using cloud AI services and generative AI. Responsibilities include building scalable pipelines, applying deep learning and neural networks, integrating models into existing systems, coordinating cross-functional teams, making technical decisions, and mentoring junior engineers.

Develop and deploy Python-based AI and data science solutions for global research modernization. Responsibilities include machine learning model development, validation, workflow automation using LLMs, data exploration, stakeholder collaboration, data governance, and communicating analytical insights. The role supports solutions from problem framing through production deployment while ensuring regulatory, ethical, and model governance compliance.

Lead end-to-end delivery and governance of enterprise AI, Generative AI, Agentic AI, and machine learning initiatives. Manage planning, budgets, resources, risks, dependencies, stakeholders, customer engagements, and cross-functional teams including architects, data scientists, engineers, and MLOps specialists. Drive discovery workshops, roadmap definition, agile delivery, executive reporting, responsible AI practices, cloud AI implementations, and production deployment while ensuring quality and business outcomes.

Drive end-to-end B2B sales and go-to-market activities for data science and applied AI solutions. Responsibilities include prospecting, customer relationship management, technical presentations, solution scoping, proposal development, business analysis, and resolving technical objections. The role partners with solution architects and data science teams to develop AI and cloud solutions, demonstrate Google Cloud integrations, manage client expectations, and identify new business opportunities across multiple industries. Travel to customer sites and events may be required.

Drives data quality, governance, security, and regulatory compliance across business units. Establishes data-management processes, implements governance decisions, improves data maturity, validates database specifications, supports data architecture and infrastructure, manages documentation and backups, and contributes to security incident remediation. The role also gathers business requirements, documents current and future processes, recommends technical improvements, and promotes data-driven decision-making in a complex insurance and financial-services environment.

Develop credit risk scorecards, predictive models, segmentation frameworks, and portfolio analytics using commercial bureau data. Analyze MSME lending portfolios, engineer features, validate models, and generate actionable risk insights. Translate client challenges into analytical solutions, support proofs of concept and pre-sales engagements, and collaborate with product, technology, and client teams. Maintain strong standards for model governance, documentation, compliance, data security, and productionisation.

Develop data-driven analytics products using large datasets, Python, SQL, statistics, and machine learning. Responsibilities include exploratory analysis, feature engineering, predictive modeling, validation, benchmarking, automated workflow development, UAT support, productization, documentation, and model governance. The role collaborates with product, technology, and business teams to deliver scalable, interpretable, and reliable decision-support solutions.

Develop data-driven risk and analytics products using bureau and financial services data. Responsibilities include exploratory analysis, feature engineering, statistical and machine learning model development, validation, benchmarking, workflow automation, UAT support, productization, documentation, and collaboration with Product, Technology, and business teams. The role requires strong Python and SQL skills, reproducible coding practices, model governance, and the ability to communicate analytical insights to technical and non-technical stakeholders.

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.

Leads OneStream-based forecasting, reporting, and demand-planning capabilities for Commercial FP&A. Builds reports and dashboards, manages data reconciliations, supports month-end reporting, optimizes platform performance, and partners with administrators and implementation teams. Drives AI and automation opportunities, delivers training, and translates financial requirements into scalable technical solutions.

Build and maintain OneStream forecasting, planning, reporting, and dashboard capabilities for Commercial FP&A. Develop demand planning models, reports, reconciliations, and performance monitoring processes while supporting month-end reporting and data improvement initiatives. Partner with finance, data, administration, and implementation teams to optimize the platform, automate processes, troubleshoot performance, deliver actionable insights, and develop internal OneStream expertise. The role also evaluates AI and automation opportunities across FP&A workflows.

Design and develop enterprise generative AI and RAG applications using LLMs, embeddings, vector databases, hybrid search, and agentic workflows. Build ingestion, retrieval, evaluation, observability, and secure API services; optimize accuracy, latency, scalability, and cost. Integrate AI solutions with enterprise systems and collaborate with technical and business stakeholders. Apply prompt engineering, context management, guardrails, security, and responsible AI practices while troubleshooting and improving production applications.

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.

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.

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.

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.