Top Data Scientist Jobs in Delhi

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.

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.

Lead end-to-end data science initiatives to design, build, deploy, monitor, and optimize security-focused AI systems. Develop ML models, LLM fine-tuning, AI agents, and partner with product, engineering, and security teams while mentoring others.

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.

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).

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 Data Scientist responsible for building and maintaining data solutions using Snowflake, Python and AWS (S3). Must collaborate with DevOps/Control‑M for developer workflows, communicate clearly, work independently, and apply problem solving. Data modeling and financial domain knowledge are desirable.

Perform EDA and analyze large retail datasets to develop, deploy, and monitor predictive models. Build data pipelines with Spark, collaborate with ML engineers and stakeholders, and communicate insights to non-technical audiences.

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.

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.

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.

Six-month junior fixed-term role to design and develop LLM-based agents for Web3 scenarios, build token research and summarization agents, assist in trading agent development using market and on-chain data, and experiment with prompt engineering, memory, and multi-agent orchestration and evaluation.

Build and productionize quantitative models and ML pipelines for equity research. Source and engineer financial features, develop predictive models, collaborate with analysts to translate methodologies, maintain data pipelines, and document model assumptions to institutional standards.

Early-career role building and evaluating LLM-powered agentic trading systems for crypto markets. Develop pipelines, prompt strategies, retrieval, model evaluations, and ship Python code for production features.

Early-career intern will support AI projects for compliance and risk (KYC, KYB, EDD). Tasks include testing and optimizing LLM/Agent workflows, reviewing prompts and model outputs, documenting findings and edge cases, supporting small-scale implementations, and performing operational analysis to improve processes.

Participate in design and development of AI-powered financial trading agents using Web3 data. Perform post-training (SFT, RLHF/RLAIF) of LLMs, build benchmark datasets, iterate workflow prototypes, and assist senior engineers with system design, integration, and deployment.

Internship role to develop and deploy user-growth algorithms: user segmentation, recommendations, LTV/churn/conversion models, A/B testing, causal inference analyses, and collaborate with product and engineering to drive measurable growth.

Lead end-to-end analytics and ML for growth: LTV, retention, segmentation, attribution, and predictive models. Partner with Paid Media, Product, and Engineering to build production-ready ML pipelines, inform SEM and bidding, monitor product metrics, and present actionable insights to stakeholders.

Lead end-to-end data science initiatives across pay TV, digital products, media sales, and customer lifecycle. Design, deploy, monitor ML/AI solutions (churn, propensity, personalization, recommendation, forecasting), partner with stakeholders, build data pipelines, run experiments (A/B, uplift), mentor teams, and translate analytics into measurable commercial impact.

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