Sr. Data Scientist - Credit Risk

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Purpose Financial, Inc. is an innovative consumer financial services company that offers a diverse suite of credit products, promoting financial inclusion and meeting consumers wherever they are. Through its brands, the company is committed to helping customers achieve their version of financial stability in the moment and in the future. Since 1997, Purpose Financial has been a pioneer in the consumer credit and financial services market offering money solutions in over 800 storefronts locations and online lending.

Providing services in over 23 states, Purpose Financial employs over 2,500 team members.

At Purpose Financial we are always on the lookout for motivated individuals who share in our values of mutual respect to join our team of outstanding professionals.

We offer:

• Competitive Wages
• 401(k) Savings Plan with Company Match
• Company Paid Holidays
• Tuition Reimbursement
• Business Casual Environment
• Rewards & Recognition Program
• Employee Assistance Program
• Office in downtown Greenville that offers free parking, onsite gym, free snacks/drinks

Purpose Financial is seeking a Senior Data Scientist, Credit Risk to serve as the dedicated modeling and analytics resource for our Line of Credit (LOC) products. This role owns the credit risk analytics agenda for our LOC products end to end: acquisition scoring, initial line assignment, line management, utilization and draw behavior, loss forecasting, and portfolio performance monitoring across Storefront, Digital, and Lead Generation channels.

The ideal candidate has built credit models for revolving or line-based products, and can translate borrower behavior into line strategy, credit policy, and forecasted financial outcomes.

This role sits within Credit Risk and Data Science, reports to the Director of Data Science & Credit Risk. and partners closely with Finance, Digital Operations, Collections, Product, and Compliance. The successful candidate is comfortable operating with a high degree of independence, moving from ambiguous business question to defensible analytical answer, and presenting that answer to senior leadership.

• Serve as the dedicated credit risk data science resource for the LOC portfolio, owning the model and analytics roadmap for the product.
• Develop, validate, and maintain machine learning and statistical models across the LOC customer lifecycle, including application scoring, initial line assignment, line increase and line decrease strategy, reauthorization, and behavioral scoring.
• Build and refine LOC-specific risk metric.
• Design and execute champion/challenger tests and controlled experiments to optimize line assignment, fee structure, reauthorization criteria, and offer terms across customer segments and origination channels.
• Produce and defend loss forecasts for the LOC portfolio, including vintage curve development, roll rate analysis, and survival or hazard-based approaches suited to open-ended revolving exposure where traditional vintage diagonals are less informative.
• Partner with Finance and Product on net charge-off forecasting, net yield analysis, and budget reforecast cycles, explaining variance between forecast and actual performance in terms of mix, vintage quality, and policy change.
• Recommend credit policy actions grounded in analysis, including tightening or loosening thresholds, segment-level cutoffs, and line sizing changes, and quantify the expected volume, loss, and revenue tradeoff of each action.
• Evaluate alternative and bureau data sources for incremental lift in the LOC population and integrate them into production models as they become available.
• Document models to standards consistent with model risk management expectations, supporting internal…