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 proudly includes Returnly.
Affirm’s Machine Learning Underwriting team solves problems critical to our business model - assessing creditworthiness in real time. Our innovative products necessitate the creation of novel machine learning solutions to drive both existing and new products. The Core Machine Learning Underwriting (CAML) team builds Affirm essential real-time point-of-sale 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.
What you'll do- Use Affirm’s proprietary and other third party data to develop machine learning models that predict the likelihood of default and make an approval or decline decision.
- Partner with the ML platform team to build credit specific ML infrastructure
- Research ground breaking solutions and develop prototypes that drive the future of credit decisioning at Affirm
- Implement and scale data pipelines, new features, and algorithms that are essential to our production models
- Collaborate with the engineering, credit, and product teams to define requirements for new products
- Develop credit models to maximize user loan volume while minimizing default losses
- Bachelors in a technical field with 6+ years of industry experience. Relevant PhD can count for up to 2 YOE
- Proficiency in machine learning with experience in areas such as gradient boosting, deep learning, graph based modeling, and calibration. Domain knowledge in credit risk is a plus
- Strong programming skills in Python
- Experience using large scale distributed data processing systems like Spark
- Experience using machine learning frameworks such as scikit-learn, pandas, numpy, xgboost, pytorch, and mllib
- Experience with Kubernetes, Docker, Kafka, Flink, and Airflow is a plus
- Excellent written and oral communication skills and the capability to drive cross-functional requirements with product and engineering teams
- The ability to present technical concepts and results in an audience-appropriate way
- Persistence, patience and a strong sense of responsibility – we build the decision making that enables consumers and partners to place their trust in Affirm!
Location - Remote Canada
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.
[For U.S. Candidates] All full-time jobs at Affirm (excluding interns and apprentices) are tied to a transparent grade-based pay range taking location into account. The grade for this position is noted above and in accordance with various U.S. pay transparency laws, we provide the base pay range and benefits for our U.S.-based positions in the links below:
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 2021 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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