Manager, Data Science

Our Purpose 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. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Manager, Data Science Overview The Global Business Solutions Center (GBSC), Strategy, Data & Insights team is built upon strong contributors with leadership abilities, consulting mindsets, and strong project management skills. The group acts as an internal consultant supporting the Shared Services Organization, which today supports four primary verticals: Finance, People & Capability, Sales & Marketing, and Legal, Franchise & Integrity. We’re seeking a highly skilled and motivated Manager, Datra Science to join our team. The Manager, Data Science plays a critical role in building and scaling a Data Science organization. This role sits at the intersection of business strategy, data, and storytelling—translating complex data into clear, actionable insights that inform leadership decisions and drive measurable business outcomes. The Manager will lead a team of Data Scientists in developing advanced insights, shaping data-driven narratives, and enabling decision-making—evolving the organization from descriptive reporting to proactive, strategic, and predictive analytics. This is a role for builders and collaborators—people who love blank slates, freedom to innovate, and helping teammates succeed. Job Description Role Responsibilities and tasks for the Manager, Data Science: • Lead and develop the Data Science team by setting clear priorities and performance expectations aligned to strategy, providing technical and people leadership, and promoting best practices across model development, validation, documentation, and deployment. • Drive efficiency and continuous improvement by tracking initiative success, refining processes, and implementing new methods and analytical approaches. • Partner with business leaders to translate strategy into data science use cases, delivering clearly communicated, actionable insights and serving as a trusted advisor on data driven decisions. • Oversee the design and delivery of predictive, statistical, and machine learning models, ensuring analytical approaches are explainable, scalable, and grounded in rigorous experimentation, hypothesis testing, and causal analysis to address key business questions. • Partner with data engineering, platform, and analytics teams to ensure high quality, usable data, enable production ready analytics and models, and influence data science tooling, architecture, and methodologies.