Hybrid Director of Data Science – AdTech

at TASC

• Job title: Director of Data Science – AdTech
• Job type: Full Time
• Experience level: Lead
• Salary: $170,000 - $337,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Cloud, Python, SQL
• Location requirements: Hybrid, Purchase, United States

About the role

• Director of Data Science leading technical data science initiatives for advertising products at Mastercard. Overseeing ML strategy, model deployment, and team collaboration in the Commerce Media platform.

Responsibilities

• 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.

Requirements

• 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 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.

Benefits

• 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
• many more