Top Data Science Jobs in Chennai

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.

Drive inbound and outbound sales for technology training and certification programs. Counsel prospective learners, understand career goals, recommend suitable courses, manage lead qualification through closing, conduct follow-ups across calls, WhatsApp, and email, maintain CRM records, build learner relationships, and achieve monthly enrollment and revenue targets. The role requires EdTech and IT course-selling experience, strong communication and negotiation skills, and availability for a US shift.

Drive inbound and outbound sales for technology training programs, counsel prospective learners, recommend courses based on career goals, manage lead qualification through enrollment, conduct follow-ups across phone, WhatsApp, and email, maintain CRM records, build prospect relationships, and achieve monthly sales and revenue targets.

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.

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.

The posting provides no specific responsibilities or qualifications beyond the Data Science Manager title. The role appears associated with Accenture’s Data and AI services and resources practice.

Leads clinical data science activities for clinical trials, including data management strategy, data integration, analysis, reporting, governance, quality control, and regulatory compliance. Oversees workstreams, ensures timely and high-quality deliverables, provides strategic guidance, collaborates with cross-functional stakeholders, and develops teams. Requires approximately 15% travel.

As a Clinical Data Science Programmer, you will develop and validate programming code for clinical trials, ensuring data integrity and compliance with regulatory standards while collaborating with scientists and statisticians.

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.

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 role involves leading data science initiatives, mentoring team members, deploying models, and ensuring high-quality technical solutions. Responsibilities include coding, architecture design, and client requirement translation.

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.

Perform data analysis, preprocessing, and modeling; collaborate with the data science team to generate reports and present findings; support project monitoring and resolve data challenges under senior guidance.

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.

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

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

Support Applied AI team by writing production-quality Python and optimized Snowflake/SQL queries to prepare and validate large datasets, run EDA, build evaluation datasets and benchmarking, and assist product managers and data scientists with experimentation, root-cause analysis, and ML/AI pipeline evaluation. Document and communicate findings to stakeholders.

Support AI workflows by building and optimizing Snowflake SQL, writing production-quality Python for data transformation and pipelines, curating evaluation datasets and metrics, performing exploratory data analysis to find quality issues, and partnering with product managers and data scientists to support experimentation, model evaluation, and automation.

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.

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 client-facing data science engagements by integrating and preparing datasets, applying data science methods, developing analytics code, managing delivery, mentoring analysts, and communicating insights and recommendations to clients.

Lead client-facing data science engagements: integrate and prepare datasets, apply data science methods, develop analytics code, manage project delivery, mentor analysts, and communicate insights to clients.