Director, Marketing Data Science
Lead and scale Chime's Marketing Data Science function, setting vision and strategy for marketing analytics that drives business outcomes. Manage a team of analysts using MMM, experimentation, and causal inference to inform marketing investment decisions.
Responsibilities
- Drive business outcomes through marketing analytics that deliver measurable impact
- Set and implement the vision and strategy for the Marketing Analytics team
- Lead, mentor, and scale a team of marketing analysts, fostering high standards for technical rigor and business impact
- Prioritize the team’s work and improve processes using AI
- Own the company’s marketing reporting and analysis for paid, owned, and integrated channels across acquisition, activation, engagement, retention, and LTV
- Build scalable reporting infrastructure and self-serve tools that improve marketing velocity and accountability
- Use MMM, experimental, and quasi-experimental methodologies to estimate incremental impact
- Translate complex analyses into clear and persuasive executive-level recommendations
- Partner with Marketing, Finance, and Product leadership to inform budget allocation and investment strategy
Requirements
- 10+ years of experience in analytics or a related quantitative field, with 8+ years in Marketing Analytics within a B2C technology company
- Results-oriented mindset with a bias toward action, clarity, and high standards
- Exceptional stakeholder management skills and experience influencing senior leadership and cross-functional executives
- Demonstrated record of using data to drive measurable business impact in a consumer-facing marketing team
- Experience with brand and performance marketing
- Experience leading and developing high-performing analytics teams with a track record of elevating talent and building strong culture
- Deep expertise in marketing measurement, including attribution modeling, media mix modeling, causal inference, customer lifetime value modeling
- Strong SQL proficiency and experience working with large-scale data warehouses
- Proficiency in BI and visualization tools (e.g., Looker, Tableau, PowerBI); experience improving data accessibility across organizations
- Strong statistical foundation (e.g., hypothesis testing, predictive modeling, experimental design)
- Experience with Python
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