Top Data Scientist Jobs in Delhi

Develop and maintain statistical and ML forecasting models for SKU-level demand using Python/PySpark and Databricks; analyze model performance (MAPE/bias), post-process outputs, collaborate with demand planners for explainability, produce dashboards and KPIs, and drive continuous improvement in forecasting and data models.

The Weather Data Scientist will develop and implement data assimilation pipelines for weather forecasting, focusing on observational data quality and machine learning-driven forecasts.

The Weather Data Scientist will enhance weather forecasting systems focusing on numerical prediction and machine learning datasets for India, collaborating on model building and verification.

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.

Own end-to-end analytics and modelling to drive product, growth, and retention. Scope business problems, build and deploy production predictive models, run A/B tests, create dashboards, and communicate findings to stakeholders to inform product and commercial decisions.

Design, train, validate, deploy and monitor ML models (pricing, dispatch, fraud, personalization). Perform EDA, feature engineering, statistical analysis, production-quality Python/SQL development, collaborate with product and engineering, and drive data quality, documentation, and knowledge sharing across the Data & AI domain.

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.

The Senior Data Scientist will architect real-time fraud detection systems, process large data sets, mentor teams, communicate findings, and innovate using AI technologies for effective risk management.

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 the development of analytics systems and predictive models, manage a team, preprocess data, analyze trends, and present findings.

The Data Scientist will leverage critical thinking and data analysis skills to develop Machine Learning models for detecting financial crime risks and synthesizing data requirements.

Lead development of data pipelines, statistical models, and machine-learning solutions on cloud platforms. Cleanse, integrate, and analyze large structured and unstructured datasets, produce visualizations and reports, and communicate insights to business and executive stakeholders. Mentor team members, perform QA on data, troubleshoot database issues, and propose data-driven strategies and predictive models.

Own end-to-end analytics and ML lifecycle to answer product, growth, and marketing questions. Scope problems, build and deploy production-grade predictive models (churn, conversion, spend optimisation), implement rigorous A/B testing, create self-serve dashboards, and communicate actionable insights to cross-functional stakeholders to drive retention and growth.

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.

Build, deploy, and iterate predictive models and real-time scoring pipelines using behavioral and clickstream data to improve lead quality and campaign optimization. Collaborate with marketing, media buying, and analytics teams to translate business needs into hypotheses, perform exploratory analysis, monitor model performance, and communicate statistically rigorous insights to non-technical stakeholders.

Lead a data science team to design, build, and deploy ML models and scalable PySpark data pipelines on Azure. Oversee feature engineering, model lifecycle, monitoring, performance tuning, MLOps/CI-CD, and translate business needs into analytical solutions while presenting results to stakeholders.

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.

Lead end-to-end LLM pipeline for customer service scheduling: data prep, prompt design, RAG systems, multi-agent architectures, multi-GPU deployment, evaluation pipelines, and chatbot integration to improve model quality and decision-making.

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.

Early-career data scientist role to analyze user and content data, build metrics, design recommendation strategies (recall, ranking, cold-start, diversity), run A/B tests, evaluate experiments, explore LLM/multimodal applications, and improve data pipelines and feature logging.

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 apply NLU/NLP methods (sentiment, intent, NER) to financial text streams, design ML models and time-series analyses, backtest and optimize quantitative trading strategies, and collaborate with trading teams to manage risk and improve model performance.

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.

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.