Director of Data Science (adtech)

at Mastercard · Fintech · Purchase, NY +7 · Remote · AI & Data

Director of Data Science for Mastercard's AdTech platform, focusing on leading ML initiatives for advertising products like targeting, bidding, ranking, and attribution. The role involves strategy, development, deployment, and mentorship, with a strong emphasis on production systems and ethical AI.

What you'd actually do

  1. Own the data science and machine learning strategy for Commerce Media, ensuring alignment with business objectives, platform capabilities, and long-term technical direction.
  2. 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.
  3. Serve as the technical authority for data science methodologies, modeling approaches, experimentation frameworks, and evaluation metrics.
  4. 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.
  5. Translate ambiguous business problems into well-scoped analytical and modeling approaches without requiring step-by-step direction.

Skills

Required

  • applied data science and machine learning within advertising, ad tech, or media platforms
  • machine learning theory and statistical modeling
  • leading complex, cross-functional initiatives
  • balancing innovation with rigor, scalability, interpretability, and governance
  • communicate complex technical concepts and analytical insights
  • mentorship
  • data privacy, security, and ethical AI practices
  • senior or leadership roles
  • Supervised and unsupervised learning
  • Probabilistic modeling and statistics
  • Optimization techniques
  • Model evaluation and bias considerations
  • building and deploying production-grade ML systems
  • advertising data science (audience modeling, bidding, optimization, measurement, attribution, real-time decisioning)
  • SQL
  • Python or R
  • large-scale data platforms (cloud data warehouses, distributed processing frameworks)

What the JD emphasized

  • production-grade ML systems
  • production ML systems
  • ethical AI principles

Other signals

  • leading cross-functional data science initiatives
  • deploying machine learning models
  • production-grade ML systems
Read full job description

Job Title:

Director of Data Science (AdTech)

Overview:

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.

Job Posting Window

Posting windows may change based on the volume of applications received and business necessity. Candidates are encouraged to apply expeditiously.

To find US Salary Ranges, visit People Place. Under the Compensation tab, select "Salary Structures." Within the text of "Salary Structures," click on the link "salary structures 2025," through which you will be able to access the salary ranges for each Mastercard job family. For more information regarding US benefits, visit People Place and review the Benefits tab and the Time Off & Leave tab.