As the Staff Data Scientist – Credit Risk & Propensity Models, you will lead development of statistical and machine-learning models that power credit decisioning, pricing, and lifecycle optimization for a small-business lending portfolio. You will partner with client and stakeholders across Risk, Credit Strategy, Product, Data Engineering, and MLOps to productionize models, monitor performance, and maintain strong model governance.
Numbers & Facts
Location
San Diego, California
Website
https://www.iniziopartners.com
Description
Role: Staff Data Scientist - Credit Risk & Propensity Models Location: San Diego, CA | Hybrid (2-3 days from office) Roles & Responsibilities As the Staff Data Scientist – Credit Risk & Propensity Models, you will lead development of statistical and machine-learning models that power credit decisioning, pricing, and lifecycle optimization for a small-business lending portfolio. You will build and maintain Probability of Default (PD) and related risk models, as well as propensity, conversion, and price-sensitivity models used in live decisioning. You will partner with client and stakeholders across Risk, Credit Strategy, Product, Data Engineering, and MLOps to productionize models, monitor performance, and maintain strong model governance.
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 create new insights unique to client ecosystem and drive fundamental transformation of risk models. This transformation would be the key for client to increase its market share in specific segments against incumbents.
Collaborate with Engineering and Data teams to productionize 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 tradeoffs clearly to technical and non-technical stakeholders
Contribute to documentation, model governance artifacts, and regulatory or audit-ready materials as needed
Candidate Profile
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.
Bachelors degree in a related quantitative field such as Data Science, Statistics, Mathematics, Economics, Finance, or Engineering required.
Masters 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 exp.) with similar skills at a lower band.