Top Data Science Jobs in Chennai

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.

Client-facing healthcare analytics business analyst responsible for engaging senior executives, identifying business pain points, designing demonstrable analytics solutions, supporting consultative sales, and helping shape product roadmaps. The role requires deep U.S. healthcare knowledge, claims and interoperability expertise, hands-on analytics experience, stakeholder communication, requirements documentation, product collaboration, and mentoring. Work spans healthcare data management, BI, cloud, AI, machine learning, and emerging analytics services.

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.

Analyzes air cargo industry trends, business processes, and operational needs to identify solution opportunities. Translates requirements into functional specifications and user stories, facilitates stakeholder workshops, validates solution designs, and supports proofs of concept. Collaborates with sales, architects, product managers, and delivery teams on proposals, RFPs, business cases, pricing, ROI, and market positioning. Provides air cargo domain advisory across freight forwarding, customs, handling, and last-mile logistics.

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.

Analyze large datasets to identify actionable insights, build analytical models predicting customer behavior and business events, maintain databases and data quality, advise business partners, communicate findings clearly, and promote process improvements. The role is hybrid and supports marketing, finance, and customer relationship management stakeholders.

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.

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.

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.

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.

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.

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.

Leads a team of scientific data analysts supporting Reaxys, Embase, and future AI-enabled life sciences platforms. Oversees search and API quality, data discoverability, testing, feature releases, quality standards, and customer-focused insights. Uses scientific expertise, Python and Bash scripting, databases, cloud platforms, and AI-enabled testing workflows to improve data integrity and platform performance. Coordinates stakeholders, documents work, presents findings, and drives cross-functional projects.

Leads a team of scientific data analysts supporting Reaxys, Embase, and future AI-enabled life sciences platforms. Oversees search and API quality evaluation, data discoverability, testing, feature releases, customer-focused insights, and quality standards. Uses scientific expertise, data analysis, scripting, databases, cloud platforms, and AI-enabled testing workflows to improve platform reliability and relevance. Coordinates stakeholders, advances projects, documents work, and presents recommendations for product and data improvements.

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.

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.

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.

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.