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Staff Data Scientist (Finance Analytics)

Chime · San Francisco, United States

External listingfull-time25 days ago

About The Role

Join Chime as a Staff Data Scientist focused on Finance Analytics. In this strategic role, you will partner with Strategic Finance to build a roadmap, tooling, and semantic layer that enables Finance to answer its own questions. You will work on ambiguous problems, mentor other analysts, and retain fluency in Spending's products and experimentation. This position offers a comprehensive benefits package, including medical, dental, and vision insurance, mental health support, financial planning assistance, and opportunities for social connection and celebration.

  • Construire la feuille de route, les outils et la couche sémantique qui permettent à la finance de répondre à ses propres questions.
  • Travailler sur des problèmes ambigus, décider de ce dont la finance a réellement besoin, construire une infrastructure durable.
  • Mentorer d'autres analystes en cours de route, en partageant votre expertise et vos connaissances.
  • Experience building dbt/Airflow pipelines, and fluency with AI coding/analytics tools (e.g., Claude Code, Cursor) used to build self-serve workflows for non-technical stakeholders like Finance
  • 8+ years in analytics roles spanning both finance and product analytics, with a track record of building infrastructure that scales an analytics function beyond one-off requests (FinTech, payments, or card a plus)
  • Expert-level SQL and strong proficiency in Python
  • Direct experience partnering with a Finance or Strategic Finance organization — fluent in unit economics, contribution margin, and forecasting, not just product metrics
  • Deep expertise in experimental design, A/B testing, and causal inference, plus experience applying ML techniques to business problems
  • Exceptional data storytelling at executive altitude, with experience using Hex and Looker
  • Track record as a trusted strategic partner to senior leaders (Directors/VPs), influencing roadmaps through both data and judgment, with a strong bias toward proactive problem discovery

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