Lead Data Scientist

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.

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.

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.

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 technical direction by evaluating emerging AI technologies, defining adoption patterns, and identifying opportunities to apply them to fraud prevention and security challenges.
• Contribute to Mastercard's 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.

All About You

• 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.