Staff Risk Analyst
EarnInRemote, US$170k – $210kPosted 14 April 2026
Tech Stack
Job Description
About EarnIn
As one of the first pioneers of earned wage access, our passion at EarnIn is building products that deliver real-time financial flexibility for those with the unique needs of living paycheck to paycheck. Our community members access their earnings as they earn them, with options to spend, save, and grow their money without mandatory fees, interest rates, or credit checks.
We’re fortunate to have an incredibly experienced leadership team, combined with world-class funding partners like A16Z, Matrix Partners, DST, Ribbit Capital, and a very healthy core business with a tremendous runway. We’re growing fast and are excited to continue bringing world-class talent onboard to help shape the next chapter of our growth journey.
POSITION SUMMARY
We are seeking a Staff Risk Analyst (Fraud) to partner closely with the Chief Risk Officer. This role is the single accountable owner of EarnIn's fraud risk management. You will operate as the company's fraud SME, setting direction, influencing architecture, and driving alignment across Engineering, Product, Compliance, and Fraud Ops.
The scope goes well beyond analysis — you will own the full lifecycle from strategy through implementation and operations, and your impact will be felt across every product and every customer interaction at EarnIn.
This is a remote position offering the opportunity to create meaningful impact in a dynamic, fast-paced fintech environment. The US base salary range for this full-time position is $170,000-$210,000 + equity + benefits. Our salary ranges are determined by role, level, and location.
WHAT YOU'LL DO
Fraud Identity Strategy
Own the full fraud policy lifecycle — spanning identity verification, account opening, deposits, card transactions, and key monetary actions — from signal analysis and risk design through rule implementation, monitoring, iteration, and ongoing optimization
Proactively identify emerging fraud vectors and redesign controls before losses materialize
Support AML rule design, simulation, and governance in partnership with Financial Crimes Compliance
Cross-Functional Program Leadership
Define the long-term fraud strategy that anchors roadmap priorities across Product and Engineering
Lead fraud initiatives spanning Engineering, Product, Compliance, Fraud Ops, and external partners including Visa and banking partners
Serve as the primary escalation point during fraud incidents and ATO attacks — leading real-time response across teams
Drive alignment on risk decisions that balance fraud exposure, customer experience, and regulatory requirements simultaneously
Analytics Organizational Enablement
Build and maintain fraud dashboards enabling real-time monitoring and self-serve analytics
Mentor junior analysts and build knowledge transfer programs to scale fraud capability across the organization
WHAT WE'RE LOOKING FOR
7+ years in analytics within fintech or consumer products
Bachelor’s, Master’s, or PhD, or equivalent industry experience
Demonstrated experience with SQL and have experience with Python or R
Experience with external fraud platforms such as Visa Risk Manager, Socure or equivalent
Experience with Periscope, Tableau, Databricks, Amplitude, and/or Optimizely is a plus
Demonstrated ability to lead cross-functional programs without formal authority
Proven track record of driving structural improvements in fraud outcomes across multiple domains simultaneously
Skilled at using data to identify emerging fraud trends, quantify exposure, and translate analytical findings into actionable policy and control changes
Strong understanding of how a credit card program works end-to-end — including transaction lifecycle, authorization and decline flows, dispute and chargeback processes, credit reporting obligations, and the relationship between card networks, issuing banks, and program managers
Strong communicator who can translate fraud concepts for non-technical audiences and business requirements into system ... (truncated, view full listing at source)
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