Senior Manager, Marketing Analytics

Responsibilities

• Lead and develop a team of data scientists, analytics engineers, and managers, setting clear expectations, coaching managers, and supporting hiring and career development
• Own the Marketing Analytics roadmap, balancing measurement capabilities that serve multiple businesses with the priorities of individual marketing teams
• Partner with senior Marketing, Finance, and business leaders to define success, identify the decisions analytics needs to support, and translate those needs into a prioritized plan
• Establish and improve incrementality testing and media mix modeling to estimate marketing's contribution to business results and inform budget allocation
• Guide the design, execution, and analysis of experiments, including audience and geographic holdouts
• Apply causal inference methods when randomized testing isn't feasible, and communicate the limits of the evidence
• Turn measurement findings into recommendations about where to invest, what to test next, and which marketing activities to adjust or scale
• Oversee marketing-focused analysis of retail and DTC performance, conversion funnels, customer segmentation, and targeting
• Guide analytics engineering work that makes marketing data reliable and reusable, partnering with Data Engineering and Business Intelligence teams on data models, metric definitions, quality, and reporting
• Communicate findings and recommendations to executives, explaining business implications, trade-offs, and uncertainty in clear language

Requirements

• 10+ years of relevant experience in marketing analytics, data science, or a closely related field, with a record of influencing marketing strategy and investment decisions
• 5+ years of experience leading analytics or data science teams, including experience managing and developing managers
• Deep expertise in marketing effectiveness measurement, including incrementality testing, media mix modeling, and using measurement to inform budget allocation
• Strong grounding in statistics, experimental design, and causal inference, with the judgment to choose methods suited to the business question and available data
• Hands-on proficiency in SQL and Python or R, and the technical depth to review analyses, challenge assumptions, and guide implementation
• Experience working with analytics engineers and data engineering partners to build reliable datasets and measurement capabilities
• Experience translating complex findings into clear recommendations and influencing senior stakeholders, including when evidence is incomplete or challenges existing assumptions
• Experience leading distributed teams and collaborating across functions, regions, and time zones