Director of Data Science (AdTech)

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 Director of Data Science (AdTech) Overview: The Director of Data Science, Commerce Media provides technical and strategic leadership for all data science, machine learning, and advanced analytics initiatives supporting the Commerce Media platform. This role owns the vision, execution, and evolution of data-driven decisioning across advertising products, including modeling, targeting, optimization, measurement, experimentation, and AI-powered insights. This individual brings deep expertise in applied data science within the advertising ecosystem, a strong theoretical foundation in machine learning and statistical modeling, and a proven track record of leading cross-functional data science initiatives across engineering, product, business, and platform teams. The Director of Data Science translates complex business and marketplace problems into scalable, production-grade data science solutions that drive measurable impact. The Role: • Own the data science and machine learning strategy for Commerce Media, ensuring alignment with business objectives, platform capabilities, and long-term technical direction. • Lead the design, development, and deployment of machine learning models and decisioning systems supporting advertising use cases such as targeting, bidding, ranking, forecasting, and attribution. • Serve as the technical authority for data science methodologies, modeling approaches, experimentation frameworks, and evaluation metrics. • Drive cross-team data science initiatives by partnering closely with Engineering, Product, Architecture, and Business teams to deliver end-to-end solutions from problem definition through production. • Translate ambiguous business problems into well-scoped analytical and modeling approaches without requiring step-by-step direction. • Establish and maintain best practices for the full model lifecycle, including feature engineering, training, validation, deployment, monitoring, and retraining. • Lead and evolve experimentation and measurement frameworks (e.g., A/B testing, causal inference, incrementality) to quantify business impact. • Champion AI-first development practices, responsibly integrating modern AI and ML tooling into data science workflows while maintaining rigor, interpretability, and governance. • Mentor and develop senior data scientists, setting a high bar for technical quality, business impact, and collaboration. • Communicate complex analytical findings and model outcomes clearly to non-technical stakeholders, including executives, product leaders, and commercial partners. • Partner with platform engineering to build scalable, reliable production ML systems operating in high-throughput, real-time environments. • Ensure data privacy, security, and ethical AI principles are embedded across all data science solutions. All About You: • Deep expertise in applied data science and machine learning within advertising, ad tech, or media platforms. • A strong foundation in machine learning theory and statistical modeling, with the ability to apply theory pragmatically in production environments. • Proven leadership in driving complex, cross-functional initiatives that require alignment across engineering, product, and business stakeholders. • Strong judgment in balancing innovation with rigor, scalability, interpretability, and governance. • The ability to clearly communicate complex technical concepts and analytical insights to both technical and non-technical audiences. ... (truncated, view full listing at source)