Data Analyst, Fraud Risk
ChimeSan Francisco, CA, USAUp to $185kPosted 19 March 2026
Tech Stack
Job Description
About the Role
We’re hiring a Data Analyst to join our Authentication Risk team, where you’ll play a critical role in preventing Account Takeover (ATO), reducing fraud losses, and protecting member trust. This role sits at the intersection of data, risk strategy, and product decisioning, with a strong emphasis on authentication flows, identity verification, and abuse prevention.
You’ll partner closely with Product, Engineering, Analytics, DSML, and OMX to identify emerging attack patterns, design data-backed controls, and continuously improve authentication resilience while maintaining a seamless member experience.
Your work will directly impact how we detect, prevent, and respond to authentication risk at scale helping ensure Chime remains both secure and easy to use for millions of members.
The base salary offered for this role and level of experience will begin at $133,000.00 and up to $185,000.00. 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
Analyze authentication and ATO-related risk signals to proactively identify fraud trends, abuse patterns, and control gaps, balancing loss reduction with member experience (40%)
Partner with cross-functional teams to design, test, and deploy authentication and ATO prevention strategies, including product and policy changes across login, recovery, and verification flows (40%)
Build and maintain dashboards and reporting to monitor authentication health, ATO rates, fraud losses, and member impact metrics (10%)
Support ad-hoc risk investigations and analysis related to authentication abuse, incidents, andf emerging threats (10%)
To thrive in this role, you have
5+ years of experience in Fraud, Risk, Compliance, or Investigations, with exposure to ATO or authentication-related risk
2+ years of hands-on analytics experience, ideally in FinTech or a high-scale consumer platform
Strong SQL skills and the ability to work comfortably with large, complex datasets
Solid understanding of authentication mechanisms, account security risks, and industry fraud trends
Experience developing or influencing risk strategies, policies, or controls
High attention to detail with a strong bias toward structured, data-driven problem solving
Ability to operate in a fast-paced environment, manage ambiguity, and deliver high-quality work under tight timelines
Bachelor’s or Master’s degree in a quantitative field (engineering, statistics, math, economics preferred)
A strong alignment with Chime’s mission and a passion for protecting members from fraud
#LI-Hybrid #LI-EI1
A little about us
At Chime, we believe that everyone can achieve financial progress. We created Chime—a financial technology company, not a bank*—on the premise that core banking services should be helpful, easy, and free. Through our user-friendly tools and intuitive platforms, we empower our members to take control of their finances and work towards their goals. Whether it's starting a savings account, purchasing a first car or home, launching a business, or pursuing higher education, we're proud to have helped millions unlock their financial potential.
We're a team of problem solvers, dreamers, and builders with one shared obsession: our members. From day one, Chimers have worked tirelessly to out-hustle and out-execute competitors to bring our mission to life. Their grit and determination inspire us to work harder every day to deliver the very best experience possible. We each bring an owner's mindset to our work, refusing to be outdone and holding ourselves accountable to meet and exceed the highest bars for our teams, our company, and our members.
We believe in being bold, dreaming big, and taking risks, while also working together, embracing our diverse perspectives, and giving each other honest feedback. Our culture remains deep ... (truncated, view full listing at source)
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