Top Data Science Jobs in Chennai

Leads and scales U.S. Bancorp India’s enterprise Data, AI, and Analytics capability. Responsibilities include building multidisciplinary teams, consolidating analytics work, managing the business-facing AI lifecycle, establishing governance and responsible AI standards, overseeing data products and MDM, driving portfolio execution, managing workforce strategy and vendor transitions, and partnering with global business, technology, risk, compliance, and audit leaders.

Define and lead scalable data architectures, including data modeling, storage, integration, and cloud platforms. Develop and operationalize AI and machine learning solutions using deep learning, generative AI, and large language models. Build data, DevOps, and MLOps pipelines; collaborate with business and technical stakeholders; establish architecture standards; solve complex client problems; and mentor team members.

Lead complex data discovery, enrichment, curation, and AI-driven research initiatives. Develop scalable workflows for entity resolution, information extraction, taxonomies, ontologies, and knowledge graphs. Collaborate with product teams, customers, and stakeholders to translate data insights into product improvements. Manage end-to-end projects, establish quality standards, mentor junior staff, and drive continuous process improvement.

Leads complex data discovery, enrichment, curation, and AI-driven research initiatives. Develops data pipelines, enrichment models, taxonomies, ontologies, knowledge graphs, and intelligent workflows. Partners with product teams and customers to translate insights into product improvements, manages end-to-end projects, establishes quality standards, mentors junior colleagues, and drives continuous process improvement.

Leads data and information management activities, including database administration, data analysis, data quality, database management, architecture, reporting, and process administration. Supervises and develops teams, directs daily work, resolves data and process issues, supports workflow improvements, implements analytical strategies, assesses risk, and ensures compliance with applicable policies and regulations.

Designs, develops, tests, delivers, and operates scalable backend Java services for PayPal’s real-time fraud detection and payments risk systems. Builds REST APIs and microservices, improves reliability and performance, contributes to automated testing and continuous integration, participates in code reviews, guides junior engineers, collaborates cross-functionally, and helps shape technical direction while maintaining highly available services.

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.

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.

Manage and evaluate scientific manuscript submissions in computer science, electrical engineering, data science, machine learning, and related fields. Coordinate peer review, provide author support, recruit research papers through scientific outreach, attend conferences and academic visits, and help strengthen journal visibility. The editor will also contribute to journal strategy, science communication initiatives, and collaborative editorial planning.

Design and build predictive and prescriptive models (churn, segmentation, survival, forecasting), run experiments (A/B, multi-armed bandit), deploy cloud-based solutions (PySpark on AWS/GCP), synthesize insights, and collaborate cross-functionally to inform marketing, finance, and operations decisions.

Designs, deploys, and manages end-to-end AI/ML, Generative AI, and Agentic AI solutions for telecom financial operations. Builds data pipelines, advanced SQL queries, statistical and machine learning models, and MLOps/LLMOps workflows using Python, GCP, BigQuery, and related tools. Translates model outputs into actionable insights for finance leaders while automating forecasting, profitability, credit, cash flow, and other financial processes.

Investigate complex consumer business questions using SQL, statistical analysis, and exploratory data analysis; communicate findings and recommendations to stakeholders; and transform validated insights into governed LookML semantic models. Build scalable BigQuery data models, Looker dashboards, conversational analytics interfaces, and AI-enabled analytical workflows. Collaborate with business, data science, and engineering teams while ensuring data quality, metric consistency, and reliable self-service reporting.

Deploy and support Snowflake data solutions, resolve production issues, perform data validation and quality checks, configure and maintain tools, and support data governance and compliance. The role also contributes to basic data modeling and dashboard development as needed in a sponsor-dedicated clinical analytics environment.

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.

Develop and validate statistical programming code for clinical trial data analysis and reporting. Collaborate with data scientists and statisticians, ensure compliance with industry and regulatory standards, document programming processes and outputs, and review analysis datasets and final reports for accuracy and completeness.

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.

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.

Leads clinical data analysis and interpretation to support drug development and patient outcomes. Oversees data collection, management, reporting, and implementation of data science best practices in clinical research. Mentors data scientists, collaborates with internal and external stakeholders, ensures data integrity and regulatory compliance, and translates complex clinical data into actionable insights.

Lead data science projects: analyze banking data, build ML solutions, design data displays, consult with stakeholders, manage requirements-to-implementation, coach junior staff, and ensure compliance and data quality.

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.

Senior Executive to lead client relationships, shape EXL's data and analytics strategy, drive generative AI adoption, oversee delivery and growth across industries, and represent the firm with C-suite clients.

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.