Senior Data Scientist, Growth Product

About the role

We're looking for a Sr. Growth Product Data Scientist to partner with our Growth Product and Engineering teams to drive measurable impact across the member journey — from acquisition to conversion through long-term retention. You'll turn data into the decisions that shape how millions of members discover, direct deposit, and get value from Chime.

This role sits at the intersection of product, data, and experimentation. You'll own funnels end-to-end, design and analyze A/B tests, and translate ambiguous business questions into clear, prioritized recommendations that ship.

In this role, you will work closely with product managers, design, engineers, product & lifecycle marketing, and operational stakeholders to foster a data-driven product development culture, advise our product roadmaps, and build a deep understanding of member behavior.

The base salary offered for this role and level of experience will begin at $133,000 and go up to $185,000. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

In this role, you can expect to

• Operate as an independent thought partner to Product, Engineering, and Design — helping shape strategy, not just measure it. You’ll proactively identify opportunities and risks, framing the right questions before they’re even asked.
• Work with the team to develop a rich program of experimentation and A/B testing to improve member product experiences, including metric creation, experiment design, power analysis, and analysis of experiment results.
• 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 metrics to measure user funnels, product engagement, and retention, and use those metrics to help brainstorm potential product experiments to drive improvements in them.
• Help the team establish high-quality eventing to allow the tracking of detailed user behaviors