Machine Learning Manager, Underwriting

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. Affirm, Inc. proudly includes Affirm, PayBright, and Returnly. 

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. Affirm, Inc. proudly includes Affirm, PayBright, and Returnly. 

The Machine Learning Underwriting team builds Affirm’s essential real-time underwriting models.  These models determine who is approved for an Affirm loan, for how much, at what interest rate, and for what loan durations.  These models are central to Affirm’s continued success and growth, and need to be explainable to the user and scalable across regions.  We’re looking for a manager to conduct a group of extremely talented Machine Learning Engineers as we bring Affirm’s underwriting models to the next level.

What You’ll Do:

• Lead a team of talented machine learning engineers and wonderful people in developing new and improved underwriting models and systems.
• Drive planning and prioritization of projects based on product, technical, and business goals.
• Build relationships with people across product, engineering, and our risk analytics organizations to jointly make decisions vital to a large profitable portfolio and an inclusive lending product.
• Coach and mentor employees to increase their impact through regular performance feedback and development conversations.
• Cultivate a respectful and supportive environment for all team members.

What We Look For

• 2+ years of experience as a manager and 4+ years of experience in a machine learning role.
• Humble management which places the needs of others first, creates inclusive environments, and builds a diverse team.
• Expertise in synthesizing complex business, product, and technical requirements, and in translating them into machine learning solutions with clear deliverables and realistic timelines.
• Clarity in explaining complex machine learning projects in non-technical language to non-specialists.
• Thorough understanding of machine learning concepts such as gradient boosting, random forests, bandit problems, bootstrapping, and prediction interval estimation.
• Familiarity with object-oriented programming in Python, Spark, and SQL.  Your team will own and maintain a Python codebase.

Location - Remote U.S.

#LI-Remote

Affirm is proud to be a remote-first company! The majority of our roles are remote and can be located anywhere in the U.S. and Canada (with the exception of the U.S. Territories, Quebec, Yukon, Nunavut, and the Northwest Territories) unless the job indicates a different global location. We are currently building operations in Spain, Poland, and Australia.  Employees in remote roles have the option of working remotely or from an Affirm office in their country of hire, and may occasionally travel to an Affirm office or elsewhere for required meetings or team-building events. Our offices in Chicago, New York, Pittsburgh, Salt Lake City, San Francisco and Toronto will remain operational and accessible for anyone to use on a voluntary basis, subject to local COVID-19 guidelines.

If you got this far, we hope you're feeling excited about this role. Even if you don't feel you meet every single requirement, we still encourage you to apply. We're eager to meet people who believe in Affirm's mission and can contribute to our team in a variety of ways—not just candidates who check all the boxes.   Inclusivity:

At Affirm, People Come First is one of our core values, and that’s why diversity and inclusion are vital to our priorities as an equal opportunity employer. You can read about our D&I program here and our progress thus far in our 2020 DEI Report.

We also 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.

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