Top Data Science Jobs in India

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 strategy, roadmap, and execution for AI-powered help experiences, AI agents, and composable platforms supporting regulatory compliance and trusted commerce. Partners with engineering, design, data science, legal, policy, compliance, risk, and operations teams to translate customer and regulatory needs into scalable products. Uses data, experimentation, and customer insights to improve experiences, manage risk, and deliver reliable, explainable AI capabilities across global markets.

Build and operationalize data products for healthcare revenue cycle management. Develop Microsoft Fabric pipelines, lakehouse and warehouse models, curated datasets, predictive models, and decision-support tools. Responsibilities include data integration, quality validation, feature engineering, model evaluation, deployment, monitoring, visualization, documentation, and stakeholder collaboration while following data governance, privacy, security, and responsible AI practices.

Develop and productionize AI and data science applications using machine learning, NLP, GenAI, LLMs, RAG, and agentic AI. Build scalable enterprise solutions with LangChain and LangGraph, integrate AI services into platforms, and implement testing, CI/CD, monitoring, governance, security, and responsible AI controls. Collaborate with product, engineering, data operations, infrastructure, risk, legal, compliance, and information security teams.

Develops AI and machine learning solutions for industrial process automation, including machinery monitoring, diagnostics, failure detection, life estimation, and asset performance optimization. Responsibilities include data cleansing, feature engineering, statistical modeling, prototype development, model validation, and scalable algorithm creation. The role collaborates with customers and business units to deliver industry-specific proof-of-concept solutions and articulate customer value propositions.

Designs, develops, and maintains enterprise AI applications using full-stack technologies, large language models, prompt engineering, workflow automation, and cloud integrations. Builds secure applications with role-based access, database integrations, and scalable architectures. Participates in Agile planning, testing, code reviews, CI/CD, deployment, and production support while collaborating with product, engineering, and business stakeholders.

Senior media leader responsible for client relationship management, integrated digital strategy, campaign governance, measurement adoption, team leadership, and commercial/account profitability. Drives growth through innovative, data-driven media solutions across search, social, programmatic, video and commerce.

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 and manage data science engagements for insurance clients: provide technical direction across data engineering, modeling, visualization, Gen-AI/LLM solutions, MLOps, and reporting; run client sessions and project operations; recruit, coach, and manage offshore analytics teams to deliver client expectations.

Build and support scalable AWS data infrastructure, lakehouse platforms, pipelines, and governance tools. Manage AWS resources including S3, Redshift, Glue, EMR, and MWAA; develop ETL workflows using SQL, Spark, and Airflow; and design data models and quality practices. Collaborate with data scientists and BI engineers, automate reporting and self-service support, improve data availability, and contribute to large-scale banking data engineering initiatives.

Build, train, evaluate, and deploy machine learning and generative AI models for fraud detection, financial agents, and safety systems. Develop high-throughput data and inference pipelines using distributed technologies, optimize GPU model serving, maintain feature stores, fine-tune language models, and implement evaluation guardrails. Collaborate with architects and engineers while applying MLOps, cloud, containerization, and software engineering practices.

Develops and supports machine-learning and advanced analytics solutions for Blue Yonder’s Luminate Planning suite. Responsibilities include feature selection, model development and evaluation, data mining, data cleansing, statistical analysis, forecasting validation, API and workflow integrations, and stakeholder presentations. The role also addresses customer data and forecasting concerns, mentors junior team members, and collaborates globally across implementation, product, support, and customer teams.

Design, develop, and deploy Generative AI applications using LLMs, RAG, prompt engineering, and fine-tuning pipelines. Integrate models into production through APIs, microservices, and cloud platforms. Evaluate model performance, reduce hallucinations, and improve accuracy, safety, and latency. Implement monitoring, logging, feedback loops, and MLOps practices while collaborating with product, data science, and engineering teams. Ensure solutions follow security, privacy, and responsible AI guidelines.

Collect, clean, preprocess, and analyze structured and unstructured data. Perform exploratory data analysis, build and evaluate basic machine learning models, write SQL queries, create dashboards and visualizations, apply statistical techniques, and document findings while supporting data science projects.

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.

Architects and delivers enterprise AI platforms, advanced analytics solutions, and scalable cloud-native architectures. Translates business requirements into secure, production-ready AI systems using Azure AI technologies. Defines patterns for generative AI, machine learning, intelligent applications, governance, MLOps, and operationalization. Collaborates with business, data, engineering, and platform teams, establishes architecture standards, evaluates emerging technologies, and guides AI initiatives from ideation through implementation.

Lead data science and analytics for eBay’s Buyer Discovery and Consideration team. Analyze customer behavior, personalization opportunities, buyer growth, and product engagement to influence roadmap decisions. Apply advanced machine learning, experimentation, statistical analysis, and ecommerce expertise to generate actionable product insights. Communicate complex findings clearly and provide strategic thought leadership across product, technology, and business teams.

Lead data science and analytics for eBay’s View Item product area. Define success metrics, experimentation roadmaps, and measurement frameworks; apply statistical methods, machine learning, predictive modeling, and causal analysis to improve buyer experiences and revenue. Partner with Product, Engineering, and Business stakeholders to translate complex findings into product strategy and roadmap recommendations.

Designs, develops, deploys, and supports AI-enabled applications using prompt engineering, machine learning, and full-stack development. Builds solutions with Python, Vue, JavaScript, and TypeScript; integrates and evaluates ML models; manages deployment and troubleshooting in IIS and Windows environments; creates audit-ready technical documentation; and collaborates with engineering, product, business, and operations stakeholders on architecture, testing, debugging, and continuous improvement.

Lead presales engagement for enterprise AI training solutions worldwide. Responsibilities include analyzing client requirements, designing customized curricula, delivering demos and presentations, supporting the sales funnel, developing proposals, and coordinating transitions to delivery. The role requires strong AI, Data Science, Analytics, cloud, and GPU expertise, along with the ability to translate technical capabilities into business value for diverse stakeholders.

Owns the vision, roadmap, lifecycle, evaluation strategy, and governance for athenahealth’s Clinical Data Science product area. The role leads Scrum product teams developing machine learning and generative AI capabilities for healthcare, partnering with Engineering, Design, Data Science, Patient Safety, Legal, Sales, and marketing. Responsibilities include prioritizing backlogs, defining success metrics, evaluating AI risks, establishing release criteria, supporting responsible AI practices, and delivering safe, scalable clinical AI experiences.

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 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.