Top Data Scientist Jobs in Delhi

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 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.

Support, monitor, and improve production Agentic AI and ML models for finance. Build data pipelines, perform EDA, optimize prompts and agents, troubleshoot drift, integrate models via APIs, and produce KPI-driven insights to align models with financial business requirements.

Lead AI-driven data science initiatives: collaborate with cross-functional teams, translate business needs into technical solutions, develop and deploy advanced AI models (including agentic architectures and prompt engineering), monitor production performance, ensure data governance, and incorporate emerging AI technologies.

Lead development and deployment of advanced AI models, design agentic architectures and prompt engineering, collaborate with cross-functional teams, ensure data readiness and governance, monitor model performance, and integrate AI workflows into production for business impact.

Build and own the platform's first recommendation features using collaborative filtering, embeddings, similarity search, and ranking. Handle sparse data and cold-starts, evaluate with offline metrics and online experiments, and partner with backend engineering to serve models and set ML practices.

The Data Scientist will implement GenAI systems, build components, support project delivery, analyze findings, and deliver technical solutions, focusing on NLP and deep learning.

The Data Scientist will analyze and model data for ML systems, focusing on Personalization and Recommendation Engines, and collaborate with teams to enhance data-driven solutions.

The Data Scientist will analyze data, optimize ML systems for pricing, propose ML opportunities, collaborate with teams, and support data-driven decisions.

Build, deploy, and maintain ML and optimization models for a quick-commerce marketplace (matching, pricing, recommendations, ETA). Design experiments and causal analyses, engineer features from messy geospatial and conversational data, own production ML pipelines, monitor model health, and collaborate cross-functionally to deliver reliable, incremental improvements.

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 design, build, and deployment of scalable machine learning models using Python, SQL, and ML/DL frameworks. Process and train models on large datasets with Spark/Databricks/Hadoop, automate pipelines with Airflow and AWS EMR, and integrate models into AWS cloud applications. Drive end-to-end experiments, optimize pipelines and queries, and deliver production-grade, high-impact data science solutions.

Build data infrastructure and reliable pipelines, analyze product usage, train traditional and LLM-based models, define product metrics, run experiments, and translate findings into product and go-to-market decisions. The role combines data science, data engineering, product analytics, machine learning, and stakeholder communication, with ownership across data modeling, evaluation, experimentation, and product insight generation.

Design, prototype, and deliver agentic AI solutions using ML and generative models. Partner with product, engineering, and delivery teams to build, evaluate, and deploy LLM-based solutions, run experiments and POCs, and communicate technical recommendations to stakeholders.

The Senior Data Scientist will lead ML initiatives, develop production-grade data science solutions, mentor junior team members, and collaborate with stakeholders to address complex problems.

The Senior Data Scientist will engineer Zoom's Revenue Intelligence Engine, developing scalable systems for lead scoring and churn prediction while collaborating with various teams to provide data-driven insights.

Lead advanced analytics and ML projects: perform EDA, feature engineering, statistical modeling, and build/optimize production ML and Generative AI solutions (LLMs, embeddings, RAG). Collaborate with engineering and MLOps to deploy and monitor models, create visualizations and dashboards, translate insights for stakeholders, and mentor junior team members.

Design, build, and productionize ML models for credit scoring, fraud detection, and segmentation. Maintain model governance and documentation, develop scalable MLOps pipelines and monitoring, generate analytical insights and dashboards, and mentor junior data scientists while leading end-to-end data science initiatives.