Top Data Scientist Jobs in Delhi

Join Kroll as a Data Scientist to build and evaluate ML models, supporting deployment and monitoring, while collaborating with experienced teams in financial services and data integrity challenges.

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.

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.

Maintain, monitor, and improve production Agentic AI and ML models for finance. Perform EDA, build data pipelines in Python/SQL, design and optimize prompts and RAG/LLM pipelines, support model-serving APIs, troubleshoot model drift, and produce KPI-driven insights for operational adoption.

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.

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.

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.

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.

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.

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.

As a Data Scientist, assess business opportunities and promote data science use, developing and operationalizing analytics solutions with internal and external partners.

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.

Senior technical leader defining evaluation-driven development, modeling strategy, MLOps and platform direction on AWS. Oversees multiple agile release cycles, ensures measurable business impact, mentors data scientists, sets governance for evaluation, synthetic data, validation, cost and compliance across the ML portfolio.

Senior technical leader defining evaluation-driven development, modeling strategy, and MLOps for an AI/ML portfolio. Owns delivery across 45-day agile cycles, sets platform architecture and cost strategy on AWS, ensures governance/compliance, mentors senior teams, and demonstrates measurable business impact through rigorous evaluation and validation approaches.

Design, develop, and fine-tune vision-language and multimodal AI models. Build computer vision pipelines (classification, detection, segmentation, OCR), prepare and annotate large datasets, run experiments, optimize models for production, and collaborate with engineering teams to deploy and monitor scalable AI solutions.

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.

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.

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.