Top Data Science Jobs in India

Develop end-to-end data science and AI/ML solutions involving statistical modeling, machine learning, generative AI, LLMs, NLP, and unstructured data analytics. Build data models and algorithms, solve ambiguous business problems, collaborate with project managers and multiple teams, and deliver actionable insights using cloud platforms such as AWS, Azure, or GCP. Present findings and communicate effectively with technical and business stakeholders.

Leads end-to-end data science and AI projects, including modeling, testing, deployment, and value realization. Manages multidisciplinary teams, coordinates with Product, IT, Climate, Risk, GIS, and business stakeholders, and applies peril modeling, climate risk, insurance, and geospatial expertise. Maintains the DSAI operating model, establishes development best practices, and oversees team capability development, mentoring, and performance management.

Deliver and create online and offline data science training, mentor aspiring data professionals, develop accessible training content, solve business problems using machine learning, and promote data science skills within the learning community.

Create and deliver online and offline data science training, mentor aspiring data professionals, help students solve data problems, curate educational content, learn and evangelize new skills, and lead strategies that make data science accessible. The role requires strong machine learning, visualization, storytelling, business problem-solving, communication, and training abilities.

Design, develop, and deploy enterprise AI/ML solutions including GenAI, LLM-based RAG systems, and agentic AI. Build scalable models, integrations, monitoring, and reusable frameworks while collaborating with stakeholders for production adoption and continuous improvement.

Build and maintain scalable analytics infrastructure and predictive models; define analytics roadmap; run experiments for acquisition, churn, delinquency and profitability; create dashboards and automated reports; monitor model and business performance; ensure data accuracy and partner with cross-functional teams.

Lead design and delivery of NLP and Generative AI solutions, from problem definition through deployment. Build and fine-tune transformers and LLMs for summarization, QA, classification and chatbots, apply classical and deep learning methods, ensure scalable reproducible ML workflows in cloud environments, mentor junior data scientists, and collaborate with product and engineering to translate business problems into AI solutions.

Lead design and development of distributed microservices using Java and Spring Boot; architect scalable RESTful APIs; drive cloud-native solutions on GCP; provide technical leadership and mentoring; own application lifecycle, performance tuning, incident resolution, and CI/CD/cloud automation.

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.

The AI Capability Lead will define the AI strategy, architect enterprise AI solutions, and mentor teams while advising clients on AI implementation.

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 the architecture and implementation of agentic AI systems: multi-agent orchestration, real-time integrations, STT/NLU ingestion, LLM fine-tuning and RAG pipelines, governance and human-in-the-loop controls, and scalable, low-latency platform design to enable proactive incident triage and automated investigations.

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.

Develops full-stack AI and machine learning applications using web technologies, statistical modeling, deep learning, databases, cloud platforms, and APIs. Designs algorithms, translates technical specifications into code, performs testing and debugging, documents solutions, and improves product and system performance. Works independently on complex initiatives, collaborates with stakeholders, mentors colleagues, and may manage projects or review others’ work.

Lead end-to-end predictive modeling and experimentation for risk, segmentation, underwriting, and matching. Build, train, deploy, and monitor supervised/unsupervised/forecasting ML models and AI agents. Partner with product, engineering, and operations to embed models in production, run A/B tests and lift analyses, and mentor junior data scientists while ensuring model governance and strong statistical rigor.

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.

Design, develop, and deploy scalable AI/ML and Generative AI solutions (LLMs, RAG, agentic AI). Build AI agents, create intelligent workflows, fine-tune and monitor models, integrate with enterprise systems, and support production deployment and continuous improvement.

The Product Owner is responsible for the product vision, roadmap, and stakeholder alignment in delivering enterprise data products. This includes managing backlogs, ensuring product adoption, establishing governance, and maintaining high-quality standards.

As a Data Engineer, you will analyze and enrich structured and unstructured data, enhance data quality, and develop insights for financial strategies.

Lead Sigmoid's insurance consulting practice to define and deliver analytics and AI-led transformation across disability, life, supplemental benefits, and absence management. Drive strategy, account growth, cross-functional delivery, solution roadmaps, and thought leadership while partnering with senior client stakeholders to convert business challenges into data-driven solutions and measurable outcomes.

Develop and maintain machine learning models, statistical tools, predictive analytics, segmentation, and document classification solutions for mortgage operations. Translate business needs into modeling tasks, build experiments, support data pipelines and model deployment, and monitor model performance. Apply supervised learning, clustering, NLP, and automation techniques while maintaining documentation and audit readiness. Collaborate with Product, Engineering, Analytics, and senior data science teams in a regulated financial environment.

Build and maintain predictive and analytical models supporting mortgage marketing, origination, and servicing decisions. Conduct marketing experiments, analyze campaign performance, develop SQL and ETL workflows, build and validate campaign files, automate recurring processes, and translate business questions into actionable insights. The role partners with marketing and business teams while ensuring accurate, governed, execution-ready analytical outputs.

The Data Science Manager will lead a team to drive AI/ML initiatives, develop and deploy scalable ML solutions, and manage projects from ideation to production.

The Senior Manager will lead the Data Science and Generative AI team, defining the AI strategy and overseeing the development of AI solutions while mentoring staff and ensuring best practices in AI governance.