This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
Design and maintain the company's end-to-end AI underwriting architecture. Build and deploy borrower risk scoring and Probability of Default (PD) models. Lead feature engineering across traditional, behavioral, and alternative data sources. Develop risk-based pricing models tied to borrower risk tiers. Monitor model performance, detect drift, and drive continuous improvement. Partner with Credit Strategy to translate model outputs into lending policies. Collaborate with Data Infrastructure to ensure accurate, production-grade model deployment. Build underwriting capability across car financing, student loans, and personal loans - with SME lending to follow.
Job Responsibility:
Design and maintain the company's end-to-end AI underwriting architecture
Build and deploy borrower risk scoring and Probability of Default (PD) models
Lead feature engineering across traditional, behavioral, and alternative data sources
Develop risk-based pricing models tied to borrower risk tiers
Monitor model performance, detect drift, and drive continuous improvement
Partner with Credit Strategy to translate model outputs into lending policies
Collaborate with Data Infrastructure to ensure accurate, production-grade model deployment
Build underwriting capability across car financing, student loans, and personal loans - with SME lending to follow
Requirements:
8-12+ years in credit risk modeling, with proven production deployments
Strong ML background - gradient boosting, neural networks, logistic regression
Experience with alternative and behavioral data sources for credit decisions
Python proficiency and familiarity with ML frameworks (scikit-learn, XGBoost, TensorFlow or similar)
Track record building models for at least one of: personal loans, auto loans, student loans, BNPL, or SME lending
Understanding of model explainability requirements in a regulated environment