Top Data Science Jobs in Chennai

Lead end-to-end UX strategy and execution for complex healthcare analytics products. Drive cross-functional collaboration, market analysis, AI-driven prototyping, accessibility and privacy compliance, scale UX standards, mentor designers, and support go-to-market product positioning and roadmaps.

Support development of credit risk and business intelligence analytic solutions: build predictive models, extract and analyze large datasets, produce insights and presentations, partner with cross-functional teams, and support client engagements using statistical and big-data tools.

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.

Lead and optimize end-to-end chargeback operations, driving strategic planning, process improvements, and SLA management. Oversee cross-functional teams, integrate data science into dispute workflows, ensure compliance and risk controls, deliver client-facing reporting, and maintain SOPs/SOWs to improve efficiency, quality, and revenue recovery.

Lead design and delivery of end-to-end AI/ML and Generative AI solutions, architect scalable cloud-based platforms, integrate LLM/RAG/agentic systems, implement MLOps and governance, and mentor teams while driving AI strategy with stakeholders.

Lead and manage customer analysis deep dives, execute strategies from senior leadership, manage client communication, and mentor analysts.

Lead and manage customer analysis, mentor analysts, apply data science methods for insights, manage data science projects, and communicate findings.

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.

Analyze large datasets, build and evaluate machine learning models, prepare and transform data, and create Power BI dashboards. Apply statistical methods and A/B testing, use cloud platforms (AWS, Snowflake), leverage Generative AI/LLMs, ensure data governance and responsible AI, and present insights while collaborating in an Agile team.

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.

The Senior Data Scientist will enhance the predictive modeling capabilities of Lyric's platform, focusing on machine learning and statistical methods for supply chain optimization.

Lead insights and analysis for assigned clients using data science methods. Manage multiple analytics engagements and the delivery team, prepare and integrate datasets, communicate findings and recommendations, stay current on industry trends, and set team development and training objectives.

Lead and manage clinical external data activities: author and track Data Transfer Specifications (DTS), ensure CDISC-compliant data structures, reconcile and harmonize external datasets, oversee vendor data delivery, support eCRF/EDC builds, train teams on DTS use, and contribute to process improvements and regulatory-compliant analytics for clinical trials.

Lead development and maintenance of Data Transfer Specifications, manage clinical external data delivery, ensure CDISC compliance, support data reconciliation, oversee vendor data management, train teams, review eCRF/EDC impacts, and ensure regulatory adherence for biomarker, imaging, and eCOA data.

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 data science strategies and insights for assigned clients. Manage multiple analytics engagements and a team, integrate and prepare datasets, apply data science methods to generate recommendations, communicate findings, and set team development objectives while staying current on industry trends.

Lead insights and analysis for assigned clients by applying data science methods, managing multiple analytics engagements, preparing and integrating datasets, communicating findings, and developing the analytics team through training and escalation management.

Lead insights and analysis for assigned clients by applying data science methods, managing multiple analytics engagements, preparing and integrating datasets, communicating findings, and developing the team through training and objectives.

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

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

The Principal Data Science Engineer will leverage statistical analysis and machine learning to optimize airline revenue performance and customer experience, collaborating with teams to develop data models and algorithms, and support product development cycles.

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

Develop, test, and operationalize data science and ML/LLM solutions using transactional and big data. Build pipelines, cloud functions, and microservice integrations; enable adoption and storytelling for business stakeholders.

Work on real-world data science projects using machine learning, statistics, experiments, and data exploration. Collaborate with stakeholders, produce actionable insights, validate analyses, participate in peer review, and iterate to improve products impacting millions of customers.

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.