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.

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.

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.

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.

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.

Apply statistical analysis and machine learning to business problems: extract and transform data with SQL, build and deploy ML models (cloud preferred), monitor model performance, and communicate insights to technical and non-technical stakeholders while collaborating in agile cross-functional teams.

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.

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.

Technical lead for advanced analytics within Customer Experience: design, build, and deploy ML solutions, lead complex modeling, mentor data scientists, set modeling best practices, partner with data engineers and business leaders, and translate analytic insights for senior 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.

Design, build, and deploy Marketing Mix Models and Bayesian statistical models to measure marketing effectiveness, forecast, and inform budget allocation. Apply causal inference and experimentation, analyze large marketing datasets, build Python analytics pipelines, and produce dashboards in Power BI or Looker Studio. Communicate findings and recommendations to stakeholders.

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.

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.

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.

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.

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.

Design, develop, and manage large-scale search and content recommendation systems. Apply ML-based personalization to improve content matching and distribution. Lead core recommendation module development, define success metrics, and collaborate with content, product, and business teams to drive AI product upgrades and better user and business outcomes.

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.