Data Scientist — Credit Risk Analytics

Anya Petrov

Full-time · Mid-level · Chicago

This role is no longer accepting applications

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About the role

This is a credit risk role, so the work is more traditional than fashionable. Underwriting quality, segmentation, vintage analysis, calibration, drift, and policy conversations that are sometimes uncomfortable but usually necessary. We lend to small businesses, which means the decisions matter and the data is never as clean as anyone wishes. We are looking for someone who likes consequential statistical work and does not need it dressed up as something else.

Responsibilities

Support development and monitoring of underwriting and portfolio risk models
Analyse application, repayment, and delinquency data to identify changes in risk patterns
Build monitoring views for vintage performance, approval quality, and model drift
Collaborate with credit policy, risk operations, and compliance teams on model changes
Document methodology clearly enough for internal review and external audit support

Requirements

3–5 years of data science or quantitative analytics in lending, insurance, or another regulated decisioning domain
Python for analysis and modelling
R for collaboration with the broader risk analytics group
SQL for large-scale application and repayment data extraction
Statistics with practical depth in calibration, segmentation, and monitoring
Scikit-learn or equivalent modelling toolkit for production-minded experimentation