Data Scientist, Growth Product
Growth Product Scientist partnering with PMs, engineers, and marketers to drive acquisition, activation, and retention through experimentation, causal analysis, and self-serve analytics tooling.
About the job
Responsibilities
- Partner with Product Managers, Engineers, Designers, and Marketers on growth initiatives spanning acquisition, onboarding, activation, and retention
- Design, run, and analyze A/B tests to improve member product experiences, including metric creation, experiment design, power analysis, and analysis of experiment results; develop frameworks to prioritize the highest-leverage experiments
- Drive data-informed decision making within Growth org by equipping PMs and engineers with self-service analytics tools, and conducting ad hoc analyses and causal studies for the team
- Use advanced statistical methods for causal inference, as well as time-series and other forecasting techniques, to solve product questions for the team; occasionally apply machine learning methods for problems such as customer segmentation
- Build and maintain dashboards, KPIs, and self-serve tooling that give the team a clear view of funnel health
- Help the team establish high-quality eventing to allow the tracking of detailed user behaviors along customer journeys through Chime’s mobile app
- Translate complex analyses into clear narratives and recommendations for product and executive audiences
- Collaborate with Data Engineering to improve the quality, accessibility, and trustworthiness of growth datasets
Requirements
- 3-5 years of relevant hands-on experience in product or business analytics roles (FinTech a plus)
- Expert-level SQL ability and proficiency in Python
- Hands-on experience designing and analyzing A/B tests; solid grasp of statistical concepts (significance, power, sample size, common pitfalls)
- Demonstrated experience acting as a trusted advisor to senior cross-functional partners — influencing decisions through both data and judgment
- A strong bias toward proactive problem discovery; explore data, spot patterns, and bring forward opportunities that meaningfully change product direction
- Strong business intuition and judgment, and experience applying prioritization frameworks to your work (e.g., RICE, Eisenhower matrix)
- Strong written and verbal communication; able to translate technical concepts to non-technical stakeholders
- Familiarity with building data pipelines using tools like dbt or Airflow
- Familiarity with AI coding tools (such as Claude Code and Cursor)
- Located within commuting distance from our downtown San Francisco headquarters, and able to work in-office with our team 4 days per week
Nice-to-Haves
- FinTech experience
Skills
SQL, Python, A/B Testing, Statistical Analysis, Causal Inference, Time Series Forecasting, Machine Learning, dbt, Airflow, Data Visualization
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