Job Description

About the Role

The Sr Manager, Forecasting Analytics will be a key member of the Credit Analytics team, focusing on forecasting analytics for credit and fraud losses for a portfolio of secured and unsecured credit cards and credit builder accounts. The role will also support forecasting of various loan servicing operations activities to assist in allocation and planning of staffing and other initiatives to drive efficiencies. This is an individual contributor role committed to analytically-driven decision-making to support Self’s growth as the premier credit building platform.

What you will do:

• Perform loss forecasting for credit cards, credit builder accounts, and other consumer lending product types.
• Assume ownership of the CECL reserve model and governance processes.
• Develop and refine forecasting techniques to project delinquency, roll rates, losses and recoveries to drive business planning and tracking.
• Preparation of regular portfolio review materials for the Credit Committee.
• Develop and maintain forecasts in support of servicing operations, such as credit bureau dispute volumes, contact activity, etc.
• Deliver timely analytical support for requests from finance, bank partners, and other external data requests.
• Conduct deep dives, stress testing, and sensitivity analyses of trends, issues, and business events to assess impact on portfolio performance.
• Partner with the Credit Strategy teams to explore new approaches, models, and data sources to support innovation in risk strategies.
• Review and approve monthly credit card revenue reports for reconciliation with bank partners.
• Participate in collaboration with the data team to design data sources and data structures to enable efficient and high integrity credit risk analytics.
• Develop vintage curves of activation, utilization, losses, etc. for card unit economics modeling.

Who you are:

• Bachelor’s degree in Engineering, Computer Science, Statistics, Economics, Finance, or similar fields. Advanced quantitative degree is preferred.
• 7+ years experience in banking or consumer lending credit risk management.
• 5+ years experience with credit analytics and/or loss forecasting.
• Experience with the sub-prime consumer segment is highly desirable.
• Experience using time series analysis, statistical modelling, machine learning, vintage curves, and roll rates to analyze loan performance.
• Deep familiarity with a range of credit performance metrics used in consumer lending.
• Highly proficient with analytical tools such as SQL, R, SAS, and Python.
• Proficiency with Microsoft Excel and/or Google Sheets is required.
• Familiarity with business intelligence tools like Tableau, Power BI, Domo, etc.
• Posses a basic understanding of the regulatory environment for consumer lending.
• Excellent writing, documentation and presentation skills.
• An innate curiosity about the way things work.
• Relentless persistence in solving problems.

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