- 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 to achieve business objectives
- Partner with platform and product engineering teams to build model training, decisioning, and monitoring systems
- 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
What we look for
- 2+ years of experience as a machine learning engineer or PhD in a relevant field
- Proficiency in machine learning with experience in areas such as Generalized Linear Models, Gradient Boosting, Deep Learning, and Probabilistic Calibration. Domain knowledge in credit risk is a plus
- Strong engineering skills in Python and data manipulation skills like SQL
- Experience using large scale distributed systems like Spark or Ray
- Experience using open source projects and software such as scikit-learn, pandas, NumPy, XGBoost, Kubeflow
- Experience developing machine learning models at scale from inception to business impact
- 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!