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.
• A collaborative leadership style that emphasizes mentorship, shared ownership, and continuous improvement.
• A strong sense of responsibility for data privacy, security, and ethical AI practices.
• Experience in a senior or leadership roles, owning data science initiatives across multiple teams or domains.
• Deep understanding of machine learning theory and practice, including:
• Supervised and unsupervised learning
• Probabilistic modeling and statistics
• Optimization techniques
• Model evaluation and bias considerations
• Proven experience building and deploying production-grade ML systems, not limited to research or offline modeling.
• Strong background in advertising data science, including areas such as audience modeling, bidding and optimization, campaign measurement, attribution, or real-time decisioning.
• Demonstrated success leading cross-functional projects requiring close collaboration with engineering, product, and business teams.
• Fluency in SQL and one or more data science programming languages (e.g., Python, R), with the ability to work effectively alongside production engineers.
• 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 reasonable_accommodation@mastercard.com 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 Mastercard’s 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 Mastercard’s guidelines.
In line with Mastercard’s 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.