Data Scientist, Growth Product

Chime Chime · Fintech · San Francisco, CA · Data Analytics

Growth Product Scientist at Chime, a fintech company, focused on driving member acquisition, conversion, and retention through data analysis, A/B testing, and product recommendations. The role involves partnering with cross-functional teams, using advanced statistical methods, and building dashboards. Requires strong SQL and Python skills, with experience in product analytics and A/B testing. Familiarity with AI coding tools is a plus.

What you'd actually do

  1. Partner with Product Managers, Engineers, Designers, and Marketers on growth initiatives spanning acquisition, onboarding, activation, and retention
  2. 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
  3. 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.
  4. 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.
  5. Build and maintain dashboards, KPIs, and self-serve tooling that give the team a clear view of funnel health

Skills

Required

  • SQL
  • Python
  • A/B testing
  • statistical concepts
  • causal inference
  • time-series forecasting
  • product analytics
  • business analytics

Nice to have

  • FinTech experience
  • dbt
  • Airflow
  • AI coding tools

What the JD emphasized

  • Expert-level SQL ability and proficiency in Python
  • Hands-on experience designing and analyzing A/B tests
  • 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.
  • Strong business intuition and judgment, and experience applying prioritization frameworks to your work (e.g., RICE, Eisenhower matrix).