Senior Data Scientist, Spending

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

Senior Data Scientist role focused on product analytics and experimentation within the spending domain at Chime, a fintech company. The role involves partnering with cross-functional teams to guide product development, lead experimentation, drive roadmap ideation, and foster a data-driven culture. Requires expertise in SQL, Python/R, and BI tools, with a preference for B2C product analytics and FinTech experience.

What you'd actually do

  1. Partner widely with product, engineering, research, and design to translate your insights to guide product development. You’ll use data to help the organization understand how members are interacting with Chime and convert that to business and experience implications.
  2. Lead experimentation by providing mentorship on how they should be run, defining success metrics and data requirements, evaluating impact, and providing strategic direction.
  3. Drive roadmap, analysis and metric ideation, and strategic discussions with stakeholders.
  4. Keep a pulse on performance metrics and KPIs. You will be positioned to have a view of the business, product, and member base and encouraged to understand and explain trends.
  5. Foster a data-driven, test-and-learn culture with your passion for telling stories with data - not only surfacing insights but also presenting those insights and recommendations to encourage and inspire change.

Skills

Required

  • SQL
  • R or Python
  • BI/Visualization tools (Looker, Tableau, PowerBI, etc.)
  • Experimentation
  • Statistical analysis
  • Causal inference
  • Metric frameworks
  • Stakeholder management

Nice to have

  • B2C product analytics
  • FinTech experience

What the JD emphasized

  • 5-7 years in data-focused roles (post-internship), building analytical infrastructure and data tools that support a wide audience and facilitate decisions of trade-offs.
  • Experience leading experimentation, statistical analysis, and sophisticated measurement (e.g. causal inference) E2E to guide decision making.
  • Expertise in SQL - you innately translate business questions to queries, understand the edge cases of joins, and can explore a warehouse to find data most appropriate to the problem.
  • Familiarity in R or python - you write reproducible code and have a tendency toward automation.
  • A focus on impact - you don’t stop with just recommendations but ensure to see work through to changing the business.