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# Credit Risk Modeler (Data Scientist)

- **Company:** TNG
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Program Optimization, SQL Databases, Data Processing, Scripting
- **Published:** July 19, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/p5tw42eht0

## About the Role

* Strong quantitative and analytical skills with a degree in a quantitative discipline (Statistics, Mathematics, Applied Mathematics, Engineering, Data Science/Analytics or related field). Master degree preferred.
 * Strong data manipulation and computational skills preferably in R, Python Scripting or SQL.
 * At least 5-7 years of relevant experience in a related area.
 * Experience in risk analytics or credit risk management in consumer portfolios will be an advantage.

## Description

The role is to assist the head of credit risk in developing and reviewing the credit scoring models.

What You'll Do:

 * Develop, implement and maintain quantitative models / scorecards and systems to assess the default likelihood, repayment behavioural, affordability, recovery expectations and volatility for different segments of the retail portfolio.
 * Evaluate, implement and monitor internal and external credit scoring models and/or financial forecasting models using both traditional and other advanced predictive technologies, segmentation and optimization tools in strategy development.
 * Develop and maintain user requirements, parameters and configurations of rating systems for different customer segments.
 * Monitor, back test and report performance of the models.
 * Work closely with independent model validators to ensure adherence to the governance framework for model deployment and ensure timely closure of validation issues.
 * Active engagement with stakeholders to develop analytic solutions using outputs from such models in credit decisioning, business strategies, risk appetite setting and capital assessment.
 * Assist to identify problem segments as well as opportunities using data.


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