Senior Credit Manager
The team
Manages credit risk activities for SoFi’s lending products including Personal Loan, Student Loan Refinance, Private Student Loan, and Credit Card.
Develops and proposes value-added credit risk strategies and models.
Works collaboratively with cross-functional teams such as Business Units, Operations, Marketing, Finance, Capital Markets, Product, Engineering, Legal, and Compliance.
Drives revenue, controls risk, and provides value to the company and consumers.
What you’ll do
Innovate: Bring your brightest ideas to build algorithmic risk strategies.
Data Driven: Conduct sophisticated analysis using customer performance data, bureau attributes, and other third-party variables to solve business problems.
Iterate, Learn, Innovate: Embrace a test-and-learn mentality and data-driven decision-making.
Collaborate: Work collaboratively with business partners such as Business Units, Operations, Marketing, Finance, Legal, and Compliance to deliver successful business results.
Control the Risk and Drive Performance Outcomes: Understand credit risk and develop approaches to mitigate loss and responsibly grow revenue.
Monitor the performance of strategies and portfolios and document and communicate results.
Identify gaps/opportunities and drive actions.
Challenge the Status Quo: Challenge others, continuously raise the bar, build better processes, and attack hard problems to help build the best products in the industry.
Grow, Grow, Grow: Be inspired by dynamic leaders and our rapidly growing business.
What you’ll need
7+ years of unsecured credit risk and data science experience.
Business acumen and work experience in the consumer lending business (loans or credit cards).
Direct experience in the credit strategy analytical life cycle, including strategy and decision tree development, P&L, presentation, implementation validation, and post-implementation monitoring.
Proven analytical skills in conducting sophisticated analysis using customer performance data, bureau attributes, and other third-party variables to solve business problems.
Advanced SQL and Python skills for segmentation and vintage analysis, PD/LGD/EAD risk modeling (e.g. decision trees, logistic regression), and back-testing, rapid prototyping, feature engineering, and pipeline development/operationalization.
A demonstrated ability to synthesize and communicate analysis to business partners and senior management.
Results-driven analytical approach, eagerness to learn, and ability to work collaboratively in a fluid environment.
Experience in developing custom credit features using data sources such as internal cross-product data, bank transaction, and other alternative data.
A demonstrated ability to design and conduct statistically rigorous experiments and evaluate inferential evaluations.
Absolutely required: Advanced degree (Master’s or PhD) with a quantitative major such as Statistics, Mathematics, Engineering, or Computer Science.