Data Scientist - Credit Risk & Propensity Models

Staff Data Scientist - Credit Risk & Propensity Models

Location:

San Diego, CA | Hybrid (2-3 days from office)

• Design, develop, and maintain Probability of Default (PD) and other core credit‑risk models used in underwriting, portfolio management, and credit strategy
• Build and enhance propensity, conversion, and price‑sensitivity models to optimize funnel performance, approval rates, and expected profitability
• Partner with Risk & Analytics leadership to translate risk appetite, growth objectives, and portfolio constraints into scalable modeling solutions
• Develop segmentation frameworks and decision logic to support differentiated credit terms, limits, and pricing across customer cohorts
• Lead model validation, performance monitoring, and stability analysis, including back‑testing, drift detection, and recalibration
• Conceptualize and execute on data science research roadmap that creates new insights unique to client ecosystem and drives fundamental transformation of risk models
• Collaborate with Engineering and Data teams to product ionize models, ensure reliable execution, and support ongoing model monitoring and governance
• Support experimentation, A/B testing, and test‑and‑learn initiatives to measure model and policy impact across the customer funnel
• Communicate model design, performance, and trade‑offs clearly to technical and non‑technical stakeholders
• Contribute to documentation, model governance artifacts, and regulatory or audit‑ready materials as needed

• 7+ years of hands‑on experience building underwriting, credit‑risk, or loss‑prediction models for small‑business or commercial portfolios
• Demonstrated experience developing PD, propensity, pricing, or funnel‑optimization models used in live decisioning
• Experience supporting model deployment, execution, and ongoing monitoring in a production environment; MLOps experience is strongly preferred
• Statistical risk modeling
• Loss forecasting and portfolio analytics
• Limit assignment optimization
• Pricing and interest rate statistical simulations
• ML model deployment and governance
• Experimentation and A/B testing
• Strong SQL and Python proficiency; ability to work with large datasets and build reproducible analytical workflows
• Excellent communication, presentation, and story‑building skills in a consulting/client‑facing setup
• Demonstrated ability to lead cross‑functional initiatives end‑to‑end and coordinate with offshore delivery teams
• Bachelor’s degree in a related quantitative field such as Data Science, Statistics, Mathematics, Economics, Finance, or Engineering required
• Master’s degree in Data Science, Statistics, Economics, Finance, or a related quantitative discipline is a plus

Client is also open to hire junior candidates (4‑5 years experience) with similar skills at a lower band.