Responsibilities

• Build and maintain Marathon's marketing attribution model, identifying which touchpoints across email, SMS, push, direct mail, and care team outreach are influencing activation, empanelment, and sustained engagement
• Develop standardized campaign performance dashboards for use by the marketing team for optimization
• Own funnel analysis across the full member journey from eligibility through activation, empanelment, and sustained engagement identifying where and why members drop off
• Design and analyze incrementality tests that isolate true lift from campaigns versus organic member behavior
• Partner with the lifecycle marketing manager and marketing automation team members to define test methodology, measurement plans, and success criteria across incrementality, A/B, and other experiment types, and own the analysis and reporting that turns results into clear optimization recommendations
• Serve as the analytical connective tissue across marketing automation, engagement enablement, product, data, and engineering teams, ensuring data flows correctly and is interpreted consistently

Requirements

• Bachelors degree in marketing or related field and 3-6 years of experience in marketing analytics, growth analytics, or a related function, ideally in consumer health, digital health, or subscription context; or equivalent combination of education and experience. Proficiency in Tableau or equivalent BI tool; ability to build dashboards from scratch, not just maintain existing ones. Strong SQL skills and comfort pulling data from multiple systems and sources.

Preferred

• Working knowledge of attribution modeling methodologies and their tradeoffs
• Experience designing and interpreting A/B tests with statistical rigor
• Familiarity with marketing technology and data platforms like Iterable, Braze, or equivalent a plus)
• Ability to work cross-functionally with technical teams including data engineering and product
• Intellectually curious about why people behave the way they do, and motivated to find the data that answers that question
• Comfortable building in ambiguity, including defining the right question before answering it
• Strong communicator who can translate complex analytical findings into plain language recommendations for non-technical stakeholders
• Detail-oriented and rigorous about data quality, but pragmatic enough to ship insights before everything is perfect
• Motivated by mission; you want the work to connect to real health outcomes, not just marketing metrics