Top Data Scientist Jobs in Delhi

Lead data science efforts to design, develop, and productionize ML models, services and scalable data pipelines. Collaborate across lines of business to deliver AI-powered software solutions, perform analysis and insights, and optimize business outcomes in a secure, scalable financial-services environment.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Lead development and deployment of AI models and agentic architectures, collaborate with cross-functional teams to translate business needs into technical solutions, ensure data readiness for S4, monitor production performance, apply prompt engineering and automation, and uphold data governance and ethical AI practices.

Lead design, development, deployment, and monitoring of advanced AI/ML models and agentic solutions. Collaborate with cross-functional teams, translate business needs into technical specifications, ensure data readiness for S4, apply prompt engineering and automation, and uphold data governance and ethical standards.

Design and maintain ETL/ELT pipelines and data warehouse architecture, analyze and visualize data with SQL/Python/R and BI tools, build machine learning models and A/B tests, and partner with engineering and business teams to deliver data-driven insights for fintech use cases (fraud detection, segmentation, forecasting).

Use data and advanced analytics to solve complex business problems, design and deploy scalable ML/data products on cloud platforms, collaborate with clients and cross-functional teams, and communicate actionable insights.

Deploy alongside clients to transform heterogeneous multimodal data into semantic structures for AI reasoning. Design ontologies, segmentation, embedding and retrieval strategies, collaborate on ingestion pipelines, evaluate retrieval and reasoning performance, and translate insights into product and engineering signals.

Lead design and production deployment of end-to-end ML models (PQL scoring, churn, expansion) using MLOps. Build pipelines for dataset/versioning, monitoring, automated retraining, and model registry. Collaborate with Product, Sales, and Data Engineering, own model observability, incident response, dbt data models, and experimentation lifecycle to deliver production-grade analytics.

Designs, builds, evaluates, and deploys machine learning, deep learning, and LLM-based systems. Owns the end-to-end data science lifecycle, including problem definition, deployment, monitoring, reproducible pipelines, model validation, drift detection, and experiment tracking. Collaborates with product and domain teams to develop modeling strategies and improves system performance, reliability, documentation, and processes.

Perform exploratory analysis, build dashboards and reports (Tableau), write SQL queries, use Python for data cleaning and predictive models, collaborate with stakeholders, communicate insights, and maintain data models, pipelines, and quality checks.

The Data Analyst will analyze marketing and ecommerce data, build reporting, dashboards, and KPIs, and enhance marketing attribution modeling.

Lead and mentor a team of data scientists, overseeing ML initiatives and driving business impact through effective data science practices.

Join Kroll as a Data Scientist to build and evaluate ML models, supporting deployment and monitoring, while collaborating with experienced teams in financial services and data integrity challenges.

Design, instrument, and evaluate agentic UX for enterprise workflows using statistical rigor and ML fluency. Build offline/online evals, run experiments (A/B, holdouts), ship production code and instrumentation, run customer pilots, mentor others, and influence product decisions to improve agent behavior and commercial outcomes.

Partner with cross-functional teams to design and execute quantitative analyses, experiments, forecasting, and visualizations that influence product and business strategy. Translate data into stories and actionable recommendations, measure impact, and present findings to senior leadership.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

Assess business opportunities and promote data science adoption. Build, test, and operationalize ML and GenAI solutions; design ML/LLM pipelines (RAG, prompt engineering). Develop scalable cloud functions and microservices, integrate APIs with ApigeeX, monitor performance, and collaborate with stakeholders to drive solution adoption.

Design, build, and evaluate classical and Bayesian ML models; perform EDA, feature engineering, and data wrangling using Python and SQL; collaborate with engineers to source and validate pipelines; track and communicate model performance; recommend retraining strategies; and produce well-documented, production-ready code and notebooks.

Build and deploy production-grade generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, fine-tune and host LLMs, develop scalable FastAPI backends, optimize latency/cost, and containerize services for production.

Senior-level data scientist to build and maintain data solutions using Snowflake, Python, and AWS (S3). Responsibilities include DevOps/Control-M integration, data modeling, problem solving, independent work, and clear communication; financial domain knowledge is a plus.

Conduct research and development on LLM technologies (RAG, prompt engineering, knowledge-grounded dialogue). Improve foundation model performance via data acquisition, evaluation, SFT, reward modeling, and RL. Prototype downstream AI/agent products for Binance and track academic and industry advances.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

Conduct original research on generative AI and LLMs for crypto/finance, design and run rigorous experiments, build evaluation frameworks, develop post-training and test-time scaling methods, and collaborate with engineers to deploy research into production focused on agent reasoning, alignment, and robustness.

Early-career research role implementing and running experiments on LLM reasoning, post-training alignment, and agentic AI for crypto markets. Tasks include literature synthesis, model implementation (PyTorch/Hugging Face), experiment tracking (Weights & Biases), and collaborating with engineers to translate research into production.

Design, build, and manage large-scale search and content recommendation systems using machine learning and personalization techniques. Collaborate with content, product, and business teams to define success metrics, drive AI product improvements, and maximize content value and user impact.