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.
We’re looking for an intelligent, driven professional to join our Bank Model Risk Management (MRM) team. This team seeks to establish, maintain and oversee an effective MRM framework to identify, quantify, monitor, mitigate and report on model risk. You will have an outstanding opportunity to work cross-functionally to develop a profound understanding of models that drive critical business decisions, and add value to the Bank by mitigating risks due to ineffective model design or model misuse.
What You'll Do- Full-Stack Model Validation: Conduct rigorous, independent validations of sophisticated credit/fraud models—including machine learning and traditional statistical models—focusing on conceptual soundness, data integrity, and performance stability.
- Advanced Quantitative Monitoring: Develop automated, independent monitoring suites in Python to track KRI/KPI drift, population stability (PSI), and feature importance shifts in real-time.
- Remediation & Technical Advisory: Partner with 1st-line Model Developers to drive the remediation of validation findings, ensuring models and strategies are not only compliant but mathematically robust.
- Audit & Regulatory Liaison: Partner with Internal Audit, Internal Controls, and Compliance to facilitate the timely resolution of audit and regulatory requests.
- Affirm Bank: Work for the internal Bank team to support the build out of the Bank Model Risk Management function. Support the model validation requirements for Bank owned models.
- 7+ years of professional experience in a highly technical capacity, such as Credit/Fraud/Financial Risk Modeling, Model Validation, or Quantitative Analytics
- Deep understanding of the consumer credit lifecycle and/or fraud detection.
- Technical familiarity with loss forecasting/fraud prediction, and stress-testing frameworks.
- Expert-level proficiency in Python (specifically pandas, scikit-learn, statsmodels) for replicative modeling and backtesting.
- Mastery of SQL for wrangling large-scale, distributed datasets and performing complex data lineage audits.
- A natural problem-solver with a meticulous eye for detail, a deep curiosity for how strategies perform, and sharp critical-thinking skills.
- Exceptional interpersonal and communication skills, with a proven ability to translate complex technical ideas for any audience.
Base Pay Grade - O
Equity Grade - USA 12
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): $220,000 - $280,000
USA base pay range (all other U.S. states): $195,000 - $255,000
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 their dependents.
- Spending stipends: Monthly stipends support your tech setup, and the ability to choose health and wellness options that are right for you.
- Time off to recharge: Flexible time off and generous holiday calendars help you rest when you need to.
- Own a piece of what you build: Our employee stock purchase plan (ESPP) lets you buy Affirm stock at a discount.
We’re committed to providing an inclusive interview process, including accommodations for candidates with disabilities. If you need support, we’re happy to help.
For positions based in San Francisco or Los Angeles: Affirm considers qualified applicants with arrest and conviction records, as required by law.
By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and consent to the use of your personal information as described.
Skills Required
- 7+ years experience in Credit/Fraud/Financial Risk Modeling, Model Validation, or Quantitative Analytics
- Deep understanding of the consumer credit lifecycle and/or fraud detection
- Technical familiarity with loss forecasting, fraud prediction, and stress-testing frameworks
- Expert-level proficiency in Python (pandas, scikit-learn, statsmodels) for replicative modeling and backtesting
- Mastery of SQL for wrangling large-scale, distributed datasets and performing complex data lineage audits
- Strong problem-solving, attention to detail, and critical-thinking skills
- Exceptional interpersonal and communication skills, ability to translate complex technical ideas for any audience
What the Team is Saying
What We Do
At Affirm, we help people say yes to the things that matter with flexible, transparent ways to pay over time. No hidden fees, no compound interest, and no fine print—just a smarter way to spend.
Why Work With Us
Who is an Affirmer? /noun/ Someone who believes finance shouldn’t be complicated. We come from a diverse set of backgrounds and we’re driven by the desire to improve lives through honest financial products. We define success by challenging one another to bring our best ideas to every single project—and we have fun while doing it.
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Affirm Offices
Remote Workspace
Employees work remotely.
Affirm is a remote-first company! Our employees can work anywhere in the U.S. but if an office is more your style, we have office locations in San Francisco, Chicago, New York City, and Pittsburgh.
