Top Data Science Jobs in India

The AI Recruiter will manage the entire recruitment process for AI-related roles, collaborate with stakeholders on hiring strategies, and maintain a talent pipeline.

Design, develop, and deploy generative AI and ML/DL solutions (including RAG, conversational and multi-agent systems) for fraud operations. Build POCs and production models, implement AI pipelines, optimize performance, ensure data quality, follow MRM and Fair Lending guidelines, collaborate cross-functionally, and mentor junior staff.

The Analyst will lead analytics for Direct Mail campaigns, driving growth and analyzing data using machine learning techniques while collaborating with various teams.

The role involves leading offer optimization and data science solutions for airlines, managing end-to-end project implementation, training analysts, and enhancing revenue optimization processes.

The Support Engineer will optimize machine learning techniques, conduct data mining, handle forecasting issues, and enhance datasets while collaborating closely with engineering and product teams.

Lead and grow Gracenote's AI & Data Science teams, drive AI/NLP/Computer Vision/MLOps initiatives, deploy production ML/GenAI solutions, enforce quality and lifecycle processes, partner cross-functionally, and advance predictive analytics and research for media audience measurement.

The role involves owning product analytics for Search, designing and interpreting A/B tests, and collaborating cross-functionally to translate data insights into actionable strategies.

Develop and deploy cross-sell and upsell ML models (e.g., XGBoost) for credit-card portfolio growth and activation. Target segments/personas, recommend campaigns and merchants to drive spend, collaborate with product, operations and portfolio teams, present results to senior stakeholders, and manage project pipelines and documentation.

Design and run time-boxed experiments and develop applied ML solutions that address high-priority business problems. Collaborate with data scientists, engineers, IT, and stakeholders to deliver models, measure ROI, standardize methods, and present findings. Prioritize and manage project backlog, adapt quickly to shifting priorities, and contribute to governance, MLOps best practices, and continuous improvement.

Design, build, and validate statistical and machine learning models. Clean and transform large datasets, engineer features, evaluate and fine-tune models, and present analytics-driven insights to clients while managing deliverables and translating business requirements into analytical solutions.

Develop predictive models and segmentation frameworks to improve direct marketing performance. Perform post-campaign analysis, ad-hoc deep dives, and performance reporting. Translate business problems into analytical solutions and prepare client-ready presentations conveying actionable insights.

Build predictive models and segmentations to improve direct marketing performance; perform post-campaign analysis and ad-hoc deep dives; translate business problems into analytical solutions and present client-ready findings.

Perform marketing analytics for P&C insurance campaigns: convert business problems into analytical solutions, program in Python/SAS, run EDA, build and validate ML models, generate campaign and project performance reports, and QC results to support decision-making.

Design, build and validate credit risk scorecards (application, behavioral, collections). Extract, clean, and preprocess data using SQL; develop ML models in Python. Monitor model performance, backtest and recalibrate to meet regulatory standards. Collaborate with business teams and document modeling processes for governance and audit.

Lead development of econometric, time series, Bayesian, and stochastic models for forecasting, pricing, and customer analytics. Translate business problems into analytical frameworks, work with large-scale data (Databricks), collaborate with stakeholders, and mentor junior team members to ensure robust, actionable statistical solutions.

Lead a data science module focused on search and match: drive NLP/ML and LLM/RAG research, build scalable data products (embeddings, vector search, rerankers), collaborate with engineering/product, mentor data scientists, manage projects, and improve search quality using Solr/OpenSearch/Elasticsearch and backend services.

Perform marketing analytics for P&C insurance campaigns: translate business questions into analytical solutions, program end-to-end in Python/SAS, run EDA, build and QC statistical and machine learning models, and generate campaign performance reports to inform decision strategy.

Lead a small data science team to research and build NLP/ML solutions (LLMs, RAG, vector search) for search, ranking, and content features; collaborate with product and engineering, estimate and run projects, mentor data scientists, and implement prompt-engineering and agentic workflows to improve user experience.

Lead a data science team to design, develop, and deploy ML/NLP and LLM-based solutions (search, recommendations, content generation/moderation). Convert business requirements into scalable models and pipelines, validate models, collaborate cross-functionally, and recruit and mentor data scientists.

Lead QA for AI-driven products, designing QA strategy and frameworks (including LLM-as-a-judge), automating test case generation, overseeing model evaluation, coordinating with product and engineering, and managing and mentoring QA teams to ensure high-quality releases.

Lead hands-on credit analytics work: build and evaluate credit risk features and models, run credit policy experiments, perform portfolio analytics, mentor junior data scientists, set technical standards, and partner with Data/Engineering for data quality and governance.

Design and lead end-to-end data science solutions: translate client use cases into technical architectures, define NFRs, select optimal approaches, write and review design documents, ensure scalability/extensibility/security, guide developers, resolve code issues, run POCs, and apply MLOps and deployment best practices for predictive modeling, optimization, NLP and Generative AI projects.

Lead analytics for promotions and incentives strategy, building scalable frameworks to measure promo ROI, automate performance tracking, and apply advanced analytics/ML for segmentation, targeting, and impact measurement. Analyze seller behavior and inventory patterns, partner with cross-functional teams, and translate insights into actionable business strategies to optimize pricing and promotions at scale.

Lead design, development, and deployment of AI/ML solutions for sustainability, ESG, and H&S. Hands-on agent and LLM/BERT development, architect enterprise-grade systems, manage end-to-end ML lifecycle (data strategy, MLOps, production), advise senior clients, mentor teams, and ensure responsible AI, governance, and scalability.

Design and build ML/AI solutions and prototypes for industrial process automation. Analyze industrial data, perform feature engineering, create statistical and predictive models for equipment monitoring, failure detection, and life estimation. Collaborate with customers and business units to develop scalable, customer-centric digital solutions and integrate models into products.