# Lead Data Scientist at Mastercard

Mastercard is hiring a Lead Data Scientist in Gurugram, India. Level rates it Builds AI ●●●●; you can [apply on Mastercard](https://jobsbylevel.com/go/834b9c24-6364-42ea-b1e5-591cd92b1421?utm_medium=ai-agent).

AI Level 4, AI centrality 90 out of 100. Gurgaon, India.

## Details

- Company: [Mastercard](https://jobsbylevel.com/companies/mastercard)
- AI level: AI Level 4 (score 90 out of 100)
- Location: Gurgaon, India
- Posted: November 3, 2026
- Apply: https://jobsbylevel.com/go/834b9c24-6364-42ea-b1e5-591cd92b1421?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=lead-data-scientist-at-mastercard-c20f5d

## Prepare for this job

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## Description

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Lead Data Scientist Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Overview The Security Solutions Data Science team develops AI and machine learning capabilities that help protect the global payments ecosystem from fraud and cyber threats. Supporting Mastercard’s fraud and risk product, the team builds and enhances models that analyze billions of transactions and enable real-time detection of fraudulent activity. We are looking for a highly motivated Lead Data Scientist to drive the next generation of AI-powered fraud detection systems. This is a senior, hands-on technical role that combines deep individual contribution with technical leadership across predictive modeling, AI agents, LLM-based solutions, real-time decisioning systems, and large-scale data engineering while contributing directly to Mastercard's mission of enabling secure and trusted commerce worldwide. Role As a Lead Data Scientist, you will be expected to be highly hands-on while providing technical leadership in designing, developing, coding, testing, and deploying enterprise-scale AI solutions that enhance fraud detection and risk intelligence capabilities across Mastercard's Security Solutions portfolio. This is not just a model development role; it is a strong fit for someone who enjoys working closely with complex data, exploring patterns, understanding the business and technical problem deeply, experimenting through R&D, and bringing interdisciplinary research ideas—such as advances in NLP, deep learning architectures, graph learning, or agentic systems —into the fraud and security space. The role is also about identifying practical solutions and implementing them in scalable production systems. You will lead complex AI engineering initiatives while directly contributing to model development, experimentation, system design, and production implementation. Key responsibilities include: Lead and directly contribute to the design, development, deployment, and optimization of machine learning models for fraud detection, risk scoring, anomaly detection, and related security applications. Define, build, and guide scalable AI and ML pipeline architecture supporting model training, validation, deployment, monitoring, retraining, and continuous improvement. Hands-on design and implementation of Generative AI and Agentic AI solutions leveraging Large Language Models (LLMs), retrieval-augmented generation (RAG), reasoning and planning workflows, Chain-of-Thought-style prompting techniques, tool/function calling, LLM-based modular systems, and multi-agent orchestration. Work with large-scale transactional datasets using distributed computing technologies such as Spark and Databricks. Implement MLOps best practices, including CI/CD, model governance, observability, experimentation frameworks, and performance monitoring. Drive the design of reusable AI platforms, libraries, and engineering frameworks that accelerate AI development, improve quality, and establish best practices across teams. Set

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