Top Data Science Jobs in Chennai

Leads a data and analytics team at vice president level, coaching colleagues and delivering strategic analytics solutions. Partners with stakeholders to define complex business questions, plans analytics resources, applies advanced analytics and visualization, and communicates insights to support business decisions. The role also requires knowledge of data architecture, cloud environments, coding, emerging tools, and methodologies, with a focus on customer and commercial impact.

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 scalable bot detection and protection products for web and mobile platforms. Responsibilities include designing and implementing features, improving existing functionality, troubleshooting complex customer issues, analyzing data, enhancing JavaScript client components, and delivering clean, reliable software from conception through deployment.

Leads clinical data management teams and oversees delivery across clinical trial projects. Responsibilities include people management, resource planning, operational metrics, quality and compliance, audit and inspection readiness, risk management, client and sponsor engagement, process improvement, and technology adoption. The role requires extensive clinical data management experience, knowledge of GCP and regulatory requirements, and demonstrated leadership in global, matrixed environments.

Develops agentic AI solutions, statistical models, optimization methods, dashboards, and actionable analytics for supply chain and operations. Builds and validates models using large-scale data, integrates LLM APIs and agentic frameworks, ensures data quality, and translates business problems into analytical solutions. Partners with data scientists, engineers, and stakeholders to deliver production-grade AI products and communicate business implications.

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.

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.

Drives growth through complex, high-value sales opportunities across data, analytics, AI, and SaaS solutions. Owns the full sales cycle, expands executive stakeholder networks, supports strategic renewals, leads workshops, and communicates measurable customer value. Partners with account managers and cross-functional teams in enterprise, academic, and government environments. Extensive regional travel and engagement with senior stakeholders are required.

Drives strategic sales growth across data, analytics, AI, and SaaS solutions for academic, government, and enterprise accounts. Owns complex opportunities through the full sales cycle, expands executive stakeholder networks, supports high-value renewals, leads workshops, and communicates measurable customer value. The role requires strong commercial judgment, technical fluency, senior-level presentation skills, cross-functional collaboration, and extensive regional travel.

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.

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.

Leads clinical data science programming for clinical trials, including designing, developing, validating, and quality-checking datasets, tables, listings, and figures. Collaborates with biostatistics, data management, and clinical teams on specifications and analysis plans. Provides technical leadership, mentors junior programmers, ensures compliance with CDISC and regulatory standards, and manages competing priorities across multiple projects.

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.

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.

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 validate SAS, R, or Python programming solutions for clinical trial data analysis and reporting. Collaborate with data scientists and statisticians, follow regulatory and industry standards, document code and outputs, and review analysis datasets and final reports for accuracy and completeness.

Design, deploy, and scale production-grade machine learning, deep learning, and generative AI solutions on cloud platforms, primarily Azure. Lead LLM applications such as chatbots and agentic workflows, oversee the full ML lifecycle, ensure production reliability and governance, and partner with global business and technical stakeholders. Mentor junior team members, influence architecture and best practices, and drive AI initiatives from problem definition through measurable business impact.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.

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.

Lead data science initiatives for Quvia’s connectivity platform by developing predictive, prescriptive, forecasting, classification, and optimization models. Integrate anomaly detection, knowledge graphs, neural networks, and generative AI into products; deploy and continuously refine production-ready models; establish data quality and governance standards; document workflows; and mentor junior data scientists while collaborating cross-functionally with engineering and product teams.

Lead development and deployment of marketing measurement models (MMM, incrementality, forecasting) using Bayesian methods. Partner with AI Agents and Data Engineering to productionize agentic analytics, validate deployed tools, train teams, mentor juniors, and produce client-ready insights and GTM recommendations.

Lead design, development, and deployment of multi-agent, agentic AI solutions using LangGraph, LLM orchestration, RAG, and RL-based fine-tuning. Build scalable architectures, integrate vector databases and knowledge graphs, run PoCs to demonstrate business impact, and mentor AI engineers and data scientists for production-grade autonomous agents.

Lead research, design, and deployment of multi-agent, agentic AI systems using LangGraph and LLM orchestration. Build memory-augmented agents, implement RAG with vector DBs, apply RLHF/RLAIF, prototype PoCs, scale production architectures, and mentor AI engineers.

Lead development and deployment of marketing measurement models (MMM, incrementality, forecasting) using Bayesian methods; partner with AI agents and engineering to operationalize agentic analytics, deliver client-ready insights, train teams, and mentor junior data scientists.