Lead Data Scientist

Overview

Lead Data Scientist on the Security Solutions Data Science team, building AI and machine learning capabilities that protect the global payments ecosystem from fraud and cyber threats. Hands-on senior role combining individual contribution with technical leadership across predictive modeling, AI agents, LLM-based solutions, real-time decisioning and large-scale data engineering.

What you'll do

• 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.
• Design and implement Generative AI and Agentic AI solutions leveraging Large Language Models (LLMs), retrieval-augmented generation (RAG), reasoning and planning workflows, Chain-of-Thought-style prompting, 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 and establish best practices across teams.
• Set technical direction by evaluating emerging AI technologies, defining adoption patterns and identifying opportunities to apply them to fraud prevention and security challenges.
• Contribute to innovation efforts by publishing top-tier research papers, filing patents and translating applied AI research into scalable fraud and security solutions.
• Ensure solutions meet Mastercard standards for scalability, reliability, explainability, security, responsible AI and long-term maintainability.

What you'll need

• Demonstrated experience leading AI Engineering, Machine Learning Engineering, Data Science or applied research initiatives from concept through production deployment.
• Expert-level hands-on programming skills in Python and strong experience building production-quality software, reusable frameworks and scalable AI systems.
• Deep understanding of machine learning fundamentals and practical application of supervised learning, ensemble methods, feature engineering, model evaluation, experimentation and production model risk controls.
• Proven track record as a hands-on practitioner developing, deploying, debugging and improving machine learning solutions in production environments with measurable business impact.
• Strong hands-on experience writing, reviewing, testing and maintaining production-grade code for AI/ML applications.
• Experience working with LLMs, prompt engineering, reasoning and planning frameworks, RAG architectures, vector databases, AI agents and Generative AI application development.
• Experience with distributed computing platforms such as Spark, Databricks or similar big data technologies.
• Familiarity with cloud platforms such as Azure, AWS or GCP and associated AI/ML services.
• Strong understanding of MLOps principles, including model deployment, model monitoring, CI/CD, ML lifecycle management, governance, observability and operational reliability.
• Experience working with SQL and large-scale structured and unstructured datasets.
• Strong analytical thinking and problem-solving abilities with a passion for solving complex real-world challenges.
• Excellent communication, technical leadership and collaboration skills, with the ability to influence architecture decisions and work effectively across cross-functional teams.
• Ability to mentor engineers, raise engineering standards, and bring strong learning agility, intellectual curiosity and thought leadership in AI and machine learning.

Nice to have

• Experience developing fraud detection, risk management, trust & safety, cybersecurity or financial crime solutions.
• Hands-on experience with Hadoop, Spark, Databricks, MLflow, Agentic AI frameworks, reasoning and planning frameworks, LangChain or similar AI engineering technologies.
• Experience building Agentic AI solutions and multi-agent systems.
• Exposure to graph learning, anomaly detection, network intelligence or real-time decisioning systems.
• Publications, patents, open-source contributions, Kaggle achievements or demonstrated research experience in AI/ML.

Details

• Location: Gurgaon, India.
• All activities involving access to Mastercard assets, information and networks require abiding by Mastercard security policies, ensuring confidentiality and integrity of information accessed, reporting suspected security violations, and completing periodic mandatory security trainings.