Job Description

Afterpay’s Credit Risk Decision Scientist is responsible for the following:

• Leverage quantitative analysis and machine learning to optimize Afterpay’s credit risk strategies
• Improve Afterpay’t BNPL credit portfolio by evaluating both merchant’s and consumers’ creditworthiness on Afterpay platform; use predictive modeling and analysis to dynamically assign credit limits that minimize default risk and maximize growth
• Closely monitor loss KPIs, analyze loss trends and propose strategy enhancements to optimize approval and loss performance through comprehensive data analysis
• Manage ongoing merchant loss performance and taylor merchant-level credit strategies based on approval/loss tracking and feedback from merchant partners
• Imagine new feature ideas and help design data pipelines to incorporate the features into Afterpay’s models through working closely with the feature science teams
• Collaborate with, drive alignment on key areas with a broad range of stakeholders such as Product, Engineers, Data Scientist, Operations to achieve shared objectives, provide strategic insight and solutions.
• Support and automate reports and operation processes (Performance Monitoring, Customer Service, etc.)

Qualifications

You have:

• Quantitative background in statistics, math, business, or related disciplines. Hands-on credit strategy experience in payments, lending, banking, financial institutions etc. a plus
• 5+  year of experience in analyzing data and using database query language (e.g. SQL, etc) and data processing and analysis tools such as R/SAS etc.. Experience with Python highly preferred
• 2+ years of experience in using or developing machine learning models with large datasets
• Work experience with data visualization tools like Tableau, Looker, or Power BI.
• Strong organizational/project management skills with the ability to handle multiple projects simultaneously in a fast-paced environment with tight deadlines
• Risk domain expert with strong capability to make professional judgment and sound decision on new business cases/challenges under ambiguity and with minimal guidance
• Strong problem solving and analysis skills, combined with superb communication skills
• Self-driven with the ability to work in a self-guided manner.
• Plus if you have experience productionalizing, deploying, and training machine learning models to solve key business problems