Senior Data Scientist

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with customers, they’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 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.

Key responsibilities include:

• Developing, deploying, and optimizing machine learning models for fraud detection, risk scoring, anomaly detection, and related security applications.
• Building scalable AI and ML pipelines supporting model training, validation, deployment, monitoring, and retraining.
• Designing and implementing Generative AI and Agentic AI solutions leveraging Large Language Models (LLMs).
• Partnering with AI Engineers and Data Scientists to productionize advanced models and translate research into scalable business solutions.
• Working with large-scale transactional datasets using distributed computing technologies such as Spark and Databricks.
• Implementing MLOps best practices, including CI/CD, model governance, observability, experimentation frameworks, and performance monitoring.
• Contributing to the design of reusable AI platforms, libraries, and engineering frameworks that accelerate AI development across teams.
• Evaluating emerging AI technologies and identifying opportunities to apply them to fraud prevention and security challenges.
• Ensuring solutions meet Mastercard standards for scalability, reliability, explainability, security, and responsible AI.

The ideal candidate will have strong programming skills in Python, experience building production-quality software, and a solid understanding of machine learning fundamentals.

Preferred qualifications include experience developing fraud detection, risk management, trust & safety, cybersecurity, or financial crime solutions, as well as hands-on experience with Hadoop, Spark, Databricks, MLflow, Agentic AI frameworks, and other AI engineering technologies.