Top Data Scientist Jobs in Delhi

Build data science products from MRI’s real estate ontology and Common Data Model. Develop and validate forecasting, anomaly detection, entity resolution, recommendation, valuation, and portfolio optimization models. Prototype knowledge-graph retrieval, LLM agents, and natural-language analytics. Define semantic metrics, improve data quality, and collaborate with product, engineering, and domain experts to move successful prototypes into production.

Develop proofs of concept and production-oriented data science and AI solutions for government and organizational clients. Responsibilities include identifying use cases, preparing and assessing data, statistical modeling, forecasting, machine learning, LLM prototyping, evaluation, documentation, stakeholder communication, and client delivery. The role will establish data science capabilities for ICF’s India team and contribute to proposals, reusable processes, and project implementation.

Develop predictive models and machine learning algorithms, preprocess and explore data, create visualizations, and optimize model performance in production. Use Python to build scalable data pipelines and analytical tools, translating complex data into actionable business insights and strategic recommendations. The role requires strong statistical, analytical, and problem-solving abilities, with responsibility for supporting data-driven decision-making and operational efficiency.

Designs and delivers enterprise-scale conversational AI solutions using Dialogflow, Google CCAI, Generative AI, and UJET. Develops intelligent virtual agents, Agent Assist capabilities, GenAI conversational experiences, CCAI Insights, enterprise integrations, and continuous optimization initiatives. Requires strong Dialogflow CX/ES expertise, Google Cloud GenAI experience, webhook and fulfillment integration skills, and hands-on UJET or comparable platform experience.

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.

Evaluates AI-generated data science and quantitative reasoning outputs; develops prompts, datasets, reference solutions, and objective evaluation benchmarks. Identifies statistical errors, flawed methodologies, and unsupported conclusions while providing structured feedback to improve model accuracy. Responsibilities include dataset validation, statistical and quantitative assessment, quality assurance, technical documentation, and clear communication of analytical findings.

Designs challenging data science and machine learning tasks, builds reproducible environments and verified reference solutions, and develops automated tests and evaluation frameworks for AI-generated code. The role analyzes AI agent performance, identifies bugs, leakage, overfitting, and methodological weaknesses, documents technical requirements, and iteratively improves task reliability and complexity.

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 AI-powered applications and agentic workflows using LLMs, RAG pipelines, embeddings, APIs, and structured and unstructured data. Develop document ingestion and semantic search systems, evaluate model outputs, improve reliability and performance, and integrate AI capabilities into backend product workflows. Collaborate with engineering and product teams to solve customer problems, experiment with emerging AI techniques, and deliver production-oriented solutions.

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

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.

Develop end-to-end classification and forecasting models, including data preparation, exploratory analysis, feature engineering, model training, tuning, evaluation, visualization, and deployment support. Collaborate with data and AI engineers on productionization, monitoring, dashboards, and reproducible documentation. Apply Python, SQL, classical machine learning, time-series modeling, cloud platforms, and business-focused analytics to solve problems such as demand forecasting and churn prediction.

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.

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.

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.

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 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.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.

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.

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.

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

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.