InternFlow

Greenhouse ·

full-time

Analytics Lead, Deposit Fraud Risk

Affirm · Remote US

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. The Fraud team at Affirm Bank works cross-functionally with Machine Learning, Product, Engineering, Operations to combat fraudulent activities to foster a safe platform. This role requires abundant cross-functional partnership. This individual will be the representative of the Credit Risk org, leading the charge on refining our Identity Verification and Fraud processes as well as our Application flow. Additionally, this role will work closely with the Product and Engineering teams to improve our Fraud solution. Come join us in our mission to change consumer finance through better technology, lower costs, and increased transparency while providing the best customer experience. What You'll Do This role will take charge of owning fraud performance for depository products, including monitoring fraud losses, approval rates, false positives, customer friction, and other key risk metrics Data Pipeline, Analytics & Monitoring Build and maintain scalable data models and transformation pipelines, using tools (such as DBT) to standardize depository product, transaction, customer, fraud, and operational data. Define trusted fraud performance metrics and analytical datasets, including fraud loss rates, approval and decline rates, false-positive rates, customer friction, and operational efficiency. Develop recurring dashboards, scorecards, and monitoring frameworks to provide clear visibility into fraud performance, portfolio health, and emerging risks. Conduct deep-dive analyses to diagnose fraud losses, control gaps, false positives, portfolio shifts, and unexpected performance changes, translating findings into actionable recommendations. Fraud Strategy Building & Optimization Develop and optimize fraud rules, policies, thresholds, models, and decision strategies that balance loss prevention, customer experience, operational capacity, and product growth. Evaluate new data sources and signals to improve detection of identity risk, account takeover, device risk, transaction risk, and other fraud typologies relevant to depository products. Lead testing and performance measurement of fraud strategy changes, including rule launches, model updates, policy changes, and new product controls, with clear pre- and post-implementation evaluation. Partner cross-functionally with Product, Engineering, Machine Learning, Data Engineering, Fraud Operations, and Risk to improve fraud strategies, strengthen operational feedback loops, and support new product launches. What We Look For EXPERIENCE - 7+ years’ of Analytics experience PRODUCT KNOWLEDGE - Passion to understand how Affirm product works and a curious mindset to help change and make it more effective TECHNICAL SKILLS - Fluent in SQL and Python PEOPLE SKILLS - A team player with ability to collaborate and influence across many different teams in the organization COMMUNICATION - Ability to communicate findings and recommendations clearly to both technical and non-technical audiences EXECUTION - Able to thrive in a fast-paced environment and be responsive and available during times of peak fraud activity MULTI-TASKING - Strong time management skills and the ability to manage multiple projects and priorities RISK KNOWLEDGE - Working knowledge of the fundamentals of payment processing and an understanding of industry risk trends, including familiarity with fraud strategy development We also encourage you to read our Chief Risk Officer’s views on what makes Credit Risk Managers influential and on a Modern Risk Management System . Base Pay Grade - M Equity Grade - USA 8 Employees new to Affirm typically come in at the start of the pay range . Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills. Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.) USA base pay range (CA, WA, NY, NJ, CT): $185,000 - $245,000 USA base pay range (all other U.S. states): $164,000 - $224,000 Please note that visa sponsorship is not available for this position. #LI-Remote Remote-first with flexibility built in Affirm is proud to be a remote-first company. Most roles can be done from almost anywhere within the country of employment. Some positions may occasionally require in-person work at an Affirm office, and a few are office-based due to the nature of the work. All new hires will be invited to attend an in-person onboarding experience. Benefits designed for you Our benefits reflect our commitment to care, transparency, and flexibility. Here are a few highlights: Health coverage at no cost: We cover 100% of premiums for employees and th

RoleAnalytics Lead, Deposit Fraud Risk
CompanyAffirm
LocationRemote US
CompensationNot disclosed
Posted2026-09-29
DeadlineRolling

Typical process for this type of role

A general guide — the exact steps for this specific listing may vary; check the original posting for details.

  1. 1ApplicationSubmit your resume through the apply link.
  2. 2ScreeningRecruiter reviews your background against the role.
  3. 3AssessmentA technical test, assignment, or coding round, depending on the role.
  4. 4Interview(s)One or more rounds with the hiring team.
  5. 5OfferOffer letter with compensation and start date.

Before you apply

0/4

Analytics Lead, Deposit Fraud Risk at Affirm: frequently asked questions

What is the salary for this role?
Affirm has not stated compensation in the listing. Check the original posting or ask during the application process.
Is the Analytics Lead, Deposit Fraud Risk position remote, hybrid or onsite?
The listing marks this role as remote, with Remote US as the location.
What is the application deadline?
Affirm has not listed a fixed deadline, so apply early in case the opening is filled.
How do I apply for the Analytics Lead, Deposit Fraud Risk role?
Use the Apply button on this page. It opens the original listing on job-boards.greenhouse.io, where you submit your application with the company.

More at Affirm

Other jobs at Affirm

Internships at Affirm

See all 25 openings at Affirm

Explore Related Placements


// similar opportunities

You might also like

Analytics Lead, Full Stack

Affirm

Remote Canadafull-time
RemoteBachelor's, Master'sGreenhouseAnalytics# SQL# Python# A/B testing# Cursor+1 more

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on ...

Apply now →

Compliance Lead, Testing

Affirm

Remote USfull-time
Greenhouse

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on ...

Apply now →

Client Success Lead

Affirm

Remote USfull-time
Greenhouse

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on ...

Apply now →

Staff Product Manager, Consumer Growth Loyalty

Affirm

Remote USfull-time
Greenhouse

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on ...

Apply now →

Senior Risk Strategist - Fraud

Mercury

San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United Statesfull-time
Greenhouse

Mercury is building a complete finance stack for startups. We work hard to create the easiest and safest banking* experience possible to simplify entrepreneurs'...

Apply now →

Senior Analytics Engineer

Mercury

San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United Statesfull-time
Greenhouse

In 1989, Tim Berners-Lee wrote a proposal for CERN. CERN lost knowledge when people left, because its information was in many systems that did not connect. His ...

Apply now →