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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningdata sciencestatistical modelingsupervised learningunsupervised learningprobabilistic modelingoptimization techniquesmodel evaluationSQLPython

Soft Skills

leadershipcollaborationcommunicationmentorshipjudgmentresponsibilityinnovationshared ownershipcontinuous improvementcross-functional initiative management