Senior Data Scientist

The Financial Crime Solutions Data Science team at Mastercard is looking for a Senior Data Scientist to join the team and help develop, deploy, and support machine learning solutions that prevent financial crime across the global payments ecosystem.

This role is primarily focused on Account-to-Account (A2A) fraud, scam, and mule detection, helping financial institutions identify and stop increasingly sophisticated forms of financial crime.

The ideal candidate combines strong analytical skills with a practical mindset and is passionate about delivering solutions that create measurable value for customers.

Responsibilities:

• Configure, validate, and deploy machine learning models to customers around the world.
• Conduct Proof-of-Value exercises with customers to demonstrate model performance and business impact.
• Contribute to the research, development, and evaluation of new fraud, scam, and mule detection solutions.
• Support and improve production models as customer needs and fraud patterns evolve.
• Partner with Product, Customer Delivery, and Data Science colleagues to enhance our solutions and deliver greater customer value.
• Analyse complex datasets to identify opportunities for improved detection performance and customer outcomes.
• Contribute to a culture of learning, continuous improvement, and practical problem solving.

Requirements:

• Experience developing, evaluating, and deploying machine learning models in a commercial environment.
• Strong proficiency in Python for data science and machine learning.
• Experience working with structured and large-scale datasets.
• Ability to apply scientific thinking and sound analytical approaches to solve complex problems.
• Strong communication skills, with the ability to explain technical concepts and results to a variety of audiences.
• Curiosity and a willingness to challenge assumptions, learn new approaches, and continuously improve solutions.
• Ability to work independently while collaborating effectively across teams.
• A customer-focused mindset and an interest in understanding how data science creates real-world value.

Nice to Have:

• Experience with PySpark and distributed data processing.
• Experience building machine learning solutions for fraud, scams, mule detection, or broader financial crime use cases.
• Knowledge of the Account-to-Account (A2A) payments ecosystem.
• Familiarity with modern software development and model deployment practices.
• Experience working with payment, banking, or financial services data.