Top Data Scientist Jobs in Delhi

Build, tune, deploy, and validate machine learning models for fraud detection, risk scoring, and customer analytics in the BFSI and credit card domain. The role involves performance and stability testing, documentation, troubleshooting data and model issues, communicating findings to varied stakeholders, managing analytical projects independently, and staying current with emerging AI and ML techniques.

Owns company-wide revenue retention and add-on monetization analytics across CPaaS, AI features, Customer Success, and Finance. Builds canonical GRR, NRR, churn, and monetization metrics; applies causal inference methods to distinguish true retention drivers from bias and data artifacts; guides experimentation and forecasting; and influences cross-functional product, pricing, lifecycle, and customer success decisions without direct authority.

Owns customer growth analytics across onboarding, activation, trial-to-paid, multi-product adoption, retention, and expansion. The role develops canonical metrics, segment definitions, value models, and causal-inference standards; identifies behaviors that predict expansion; evaluates experiments; and translates product, behavioral, and revenue data into guidance for Growth, GTM, Finance, and product leaders. It also shapes event taxonomy, influences roadmap and investment decisions, and establishes reusable analytical practices across teams.

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.

Analyze billions of user behavior, fund-flow, and market records using PySpark, Hive, Trino, and SQL. Build feature pipelines, investigate data anomalies, and develop and evaluate machine learning models for risk detection. Translate model performance into business metrics such as detection rate, false-positive rate, and latency while owning a project stream end to end.

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.

Lead NLP and LLM fine-tuning initiatives using PEFT methods, curate and validate training datasets, implement RLHF and DPO alignment, optimize models through quantization, design evaluation benchmarks, manage distributed multi-GPU training, and prepare model checkpoints for cloud deployment. Requires advanced expertise in Python, PyTorch, transformer architectures, CUDA, and NLP, plus a mandatory AWS or Google Cloud machine learning certification.

Develop audience segments, customer profiles, and predictive models using behavioral, transactional, viewing, and digital data. Apply statistical and machine learning methods for clustering, churn, propensity, content affinity, and customer value analysis. Conduct exploratory analysis, evaluate models, support campaign measurement, and translate findings into business recommendations. Collaborate with Marketing, Product, Media Sales, and Content teams while presenting insights to technical and non-technical stakeholders.

Research and develop quantitative signals from high-frequency equity market data. Responsibilities include feature engineering, exploratory data analysis, simulation and backtesting, validation across markets and regimes, signal implementation, and collaboration with quantitative researchers and engineers. The role also involves applying machine learning and deep learning to market microstructure, building production-quality low-latency code, exploring data sources, and developing tools to automate software development, testing, and deployment workflows.

Build and validate production machine learning and statistical models for music and entertainment marketing. Develop real-time data systems, predictive scoring, trend detection, AI briefing classification, dashboards, and GenAI-assisted workflows. Collaborate with data engineering, paid media, and creative teams while implementing tokenization, anonymization, and cross-border data privacy controls. The role is a 12-month fixed-term contract supporting clients across APAC.

Conduct technical research at the intersection of AI and blockchain, exploring LLMs, agents, on-chain data, trading, and intelligent applications. Build reproducible prototypes and validate innovative ideas through proof-of-concepts and product demonstrations. Track industry developments, analyze research findings, document conclusions, and collaborate with algorithm and engineering teams to advance concepts toward implementation.

Build and deploy machine learning and optimization models for marketplace matching, pricing, recommendations, ETA prediction, and conversational ordering. Design experiments and causal analyses, engineer features from large-scale operational data, and own production ML pipelines through deployment, monitoring, and retraining. Collaborate with product, engineering, DevOps, and operations teams to improve marketplace performance and reliability.

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).

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.

Develop user-facing financial AI and equity research algorithms across query understanding, document processing, entity recognition, event detection, retrieval, ranking, and quality evaluation. Build datasets, labeling systems, benchmarks, and production evaluation frameworks. Apply large language models, NLP, machine learning, reinforcement learning, knowledge graphs, and multimodal methods while collaborating with engineers to deploy and continuously optimize financial data and knowledge systems.

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.

Leads biomedical data science for clinical and health research, integrating clinical, observational, EHR, genetic, wearable, electrophysiological, and video data. Develops signal-processing methods, machine-learning models, probabilistic and time-series analyses, scalable data pipelines, and reproducible workflows. Validates clinically relevant models, supports disease phenotyping and predictive modeling, ensures compliance with pharmaceutical and healthcare standards, and communicates technical findings to clients and stakeholders.

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.

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, audit, validate, optimize, and modernize statistical models written in R. Build and deploy new predictive models and analytical features, improve production code quality, collaborate with data engineering, communicate technical findings to stakeholders, and mentor junior data scientists. The role requires advanced statistical modeling expertise, strong R proficiency, and experience improving complex algorithms.

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.

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.

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