Quantitative Analytics Manager

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.

At Affirm, we are using today’s technology to bring significant innovation to the financial industry. Since day one, our mission has been to deliver honest financial products that improve lives. That mission hasn’t changed — and it never will.

We’re looking for an individual to join the Merchant Pricing team! This team is responsible for enabling efficient value exchange between Affirm’s merchants, consumers, and strategic partners. As a Quantitative Analytics Manager, you’ll help optimize our portfolio segments at scale and refine the analytical tooling to get us there. Your day-to-day focus will be to develop analytical capabilities to enhance our research infrastructure, and to apply your knowledge to improve Affirm’s loan economics. This is a high-impact role focused on making prudent decisions that drive profitable growth.

What You’ll Do

• Apply and develop AI-powered, machine learning models to simulate and analyze risk on Affirm’s loan portfolio
• Ideate, validate, implement, and track performance of pricing recommendations that were generated by discrete optimization methods
• Build expertise in our evolving data warehouse, risk models, and optimization methodologies
• Assist in launching user-level pricing experiments and monitor against model results
• Partner with our Credit, Product, Engineering, Applied Machine Learning, Commercial, Finance and Capital Markets teams on company-wide initiatives

What We Look For

• 6+ years of experience in an analytical or quantitative role
• Practical knowledge of fixed income, financial modeling, asset pricing, and consumer financial services
• Familiarity in applying Python and SQL to analyze large data sets
• PySpark experience is a plus
• Existing Github presence or portfolio of former projects
• Masters Degree in Computer Science, Mathematics, Data Science, Statistics, Finance, or Financial Engineering is a plus

Pay Grade – O Equity Grade – 6 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. CAN base pay range per year: $170,000 – $220,000 #LI-Remote

Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities.

We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include: 

• Health care coverage – Affirm covers all premiums for all levels of coverage for you and your dependents 
• Flexible Spending Wallets – generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses
• Time off – competitive vacation and holiday schedules allowing you to take time off to rest and recharge
• ESPP – An employee stock purchase plan enabling you to buy shares of Affirm at a discount

We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.

[For U.S. positions that could be performed in Los Angeles or San Francisco] Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, Affirm will consider for employment qualified applicants with arrest and conviction records.

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