Senior Data Scientist

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 Senior Data Scientist to help build the next generation of AI-powered fraud detection systems. This role offers an opportunity to work on challenging problems involving 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 Senior Data Scientist, you will design, develop, and deploy 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 ideal for someone who enjoys working closely with complex data, exploring patterns, deeply understanding business and technical problems, experimenting through R&D, and translating practical solutions into scalable fraud and security capabilities.

Key responsibilities include:

• Develop, deploy, and optimize machine learning models for fraud detection, risk scoring, anomaly detection, and related security applications.
• Build scalable AI and ML pipelines supporting model training, validation, deployment, monitoring, and retraining.
• 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 techniques, tool/function calling, LLM-based modular systems, and multi-agent orchestration.
• Partner with AI Engineers and Data Scientists to productionize advanced models and translate research into scalable business solutions.
• 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.
• Contribute to the design of reusable AI platforms, libraries, and engineering frameworks that accelerate AI development across teams.
• Evaluate emerging AI technologies and identify 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, and responsible AI.

All About You:

• Strong programming skills in Python and experience building production-quality software.
• Solid understanding of machine learning fundamentals, including supervised learning, ensemble methods, feature engineering, model evaluation, and experimentation.
• Hands-on experience developing and deploying machine learning solutions in production environments.
• 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.
• Understanding of MLOps principles, including model deployment, model monitoring, CI/CD, and ML lifecycle management.
• 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 and collaboration skills with the ability to work effectively across cross-functional teams.
• Strong learning agility, intellectual curiosity, and desire to stay current with advances in AI and machine learning.

Preferred Qualifications:

• 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.