Sr. Data Scientist - Credit Risk

Purpose Financial, Inc. is an innovative consumer financial services company offering 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 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, providing services in over 23 states with over 800 storefront locations and online lending, employing over 2,500 team members.

About the role

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.

Responsibilities

• 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 metrics.
• 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.
• Partner with Finance and Product on net charge-off forecasting, net yield analysis, and budget reforecast cycles, explaining variance between forecast and actual performance.
• 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 validation, audit, and regulatory review.
• Present findings and recommendations to the Credit Risk Review Committee and other senior audiences, translating technical work into clear business implications.
• Mentor junior data scientists and analysts on modeling technique, credit domain knowledge, and analytical rigor.
• Understand, adhere to, and enforce all corporate policies.

Requirements

• Bachelor's degree in Statistics, Economics, Mathematics, Computer Science, Engineering, or a related quantitative field. Advanced degree strongly preferred.
• At least three to five years of experience in credit risk analytics, credit modeling, or a closely related quantitative role within consumer lending. Direct experience building and deploying credit risk models in a production lending environment is required.
• Experience with revolving or line-based credit products, such as lines of credit, credit cards, or open-ended installment structures, is strongly preferred.
• Experience in the non-prime or subprime consumer segment is a significant advantage.

Skills

• Excellent written and verbal communication skills; adaptability and flexibility in a changing environment.
• Ability to understand and ensure compliance with policies, procedures, and laws governing the industry and products.
• Demonstrated ability to build production credit risk models using Python or R, including gradient boosting methods, logistic regression, and survival or time-to-event techniques.
• Working fluency with the Python data science stack, including Pandas, NumPy, scikit-learn, XGBoost or LightGBM, and SHAP or comparable explainability tooling.
• Strong SQL skills and the ability to work independently against large, imperfect transactional data.
• Understanding of consumer credit fundamentals: probability of default, exposure at default, loss given default, roll rates, vintage analysis, and reserve or allowance concepts.
• Familiarity with the regulatory environment governing consumer lending, including ECOA, Regulation B, FCRA, adverse action requirements, fair lending, disparate impact considerations, UDAAP, and model risk management expectations consistent with SR 11-7.
• Experience with reporting and visualization tools such as Tableau or Power BI.
• Experience with data engineering practices, version control, and reproducible analytical workflows is a plus.

Benefits

• Competitive wages
• Health and life benefits
• Health Savings Account plus Employer Seed
• 401(k) Savings Plan with Company Match
• Paid Parental Leave
• Company Paid Holidays
• Paid Time Off including Volunteer Time
• Tuition Reimbursement
• Business Casual Environment
• Rewards & Recognition Program
• Employee Assistance Program
• Office in downtown Greenville that offers free parking, onsite gym, free snacks/drinks

Schedule

• Business casual attire
• 0-10% travel

Other

• Must be eligible to work in the USA and able to pass a background check.