Director of Data Science (AdTech)

Our Purpose _Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, weu2019re 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: u2022 Own the data science and machine learning strategy for Commerce Media, ensuring alignment with business objectives, platform capabilities, and long-term technical direction. u2022 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. u2022 Serve as the technical authority for data science methodologies, modeling approaches, experimentation frameworks, and evaluation metrics. u2022 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. u2022 Translate ambiguous business problems into well-scoped analytical and modeling approaches without requiring step-by-step direction. u2022 Establish and maintain best practices for the full model lifecycle, including feature engineering, training, validation, deployment, monitoring, and retraining. u2022 Lead and evolve experimentation and measurement frameworks (e.g., A/B testing, causal inference, incrementality) to quantify business impact. u2022 Champion AI-first development practices, responsibly integrating modern AI and ML tooling into data science workflows while maintaining rigor, interpretability, and governance. u2022 Mentor and develop senior data scientists, setting a high bar for technical quality, business impact, and collaboration. u2022 Communicate complex analytical findings and model outcomes clearly to non-technical stakeholders, including executives, product leaders, and commercial partners. u2022 Partner with platform engineering to build scalable, reliable production ML systems operating in high-throughput, real-time environments. u2022 Ensure data privacy, security, and ethical AI principles are embedded across all data science solutions. All About You: u2022 Deep expertise in applied data science and machine learning within advertising, ad tech, or media platforms. u2022 A strong foundation in machine learning theory and statistical modeling, with the ability to apply theory pragmatically in production environments. u2022 Proven leadership in driving complex, cross-functional initiatives that require alignment across engineering, product, and business stakeholders. u2022 Strong judgment in balancing innovation with rigor, scalability, interpretability, and governance. u2022 The ability to clearly communicate complex technical concepts and analytical insights to both technical and non-technical audiences. u2022 A collaborative leadership style that emphasizes mentorship, shared ownership, and continuous improvement. u2022 A strong sense of responsibility for data privacy, security, and ethical AI practices. u2022 Experience in a senior or leadership roles, owning data science initiatives across multiple teams or domains. u2022 Deep understanding of machine learning theory and practice, including: u2022 Supervised and unsupervised learning u2022 Probabilistic modeling and statistics u2022 Optimization techniques u2022 Model evaluation and bias considerations u2022 Proven experience building and deploying production-grade ML systems, not limited to research or offline modeling. u2022 Strong background in advertising data science, including areas such as audience modeling, bidding and optimization, campaign measurement, attribution, or real-time decisioning. u2022 Demonstrated success leading cross-functional projects requiring close collaboration with engineering, product, and business teams. u2022 Fluency in SQL and one or more data science programming languages (e.g., Python, R), with the ability to work effectively alongside production engineers. u2022 Experience working with large-scale data platforms, such as cloud data warehouses and distributed processing frameworks. Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly. Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: + Abide by Mastercardu2019s security policies and practices; + Ensure the confidentiality and integrity of the information being accessed; + Report any suspected information security violation or breach, and + Complete all periodic mandatory security trainings in accordance with Mastercardu2019s guidelines. In line with Mastercardu2019s total compensation philosophy and assuming that the job will be performed in the US, the successful candidate will be offered a competitive base salary and may be eligible for an annual bonus or commissions depending on the role. The base salary offered may vary depending on multiple factors, including but not limited to location, job-related knowledge, skills, and experience. Mastercard benefits for full time (and certain part time) employees generally include: insurance (including medical, prescription drug, dental, vision, disability, life insurance); flexible spending account and health savings account; paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave); 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro-rated based on date of hire; 10 annual paid U.S. observed holidays; 401k with a best-in-class company match; deferred compensation for eligible roles; fitness reimbursement or on-site fitness facilities; eligibility for tuition reimbursement; and many more. Mastercard benefits for interns generally include: 56 hours of Paid Sick and Safe Time; jury duty leave; and on-site fitness facilities in some locations. Pay Ranges Purchase, New York: $195,000 - $323,000 USD Arlington, Virginia: $196,000 - $323,000 USD Austin, Texas: $170,000 - $281,000 USD Boston, Massachusetts: $196,000 - $323,000 USD New York City, New York: $204,000 - $337,000 USD O'Fallon, Missouri: $170,000 - $281,000 USD Remote - New York: $170,000 - $281,000 USD San Francisco, California: $204,000 - $337,000 USD Job Posting Window Posting windows may change based on the volume of applications received and business necessity. Candidates are encouraged to apply expeditiously.

Created: 2026-03-23