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# CECL Model Development & Implementation Lead

- **Company:** EVERBANK
- **Location:** Rancho Cordova, CA, United States
- **Experience:** Expert
- **Salary:** $143,581.0 - $194,257.0
- **Contract:** Permanent contract
- **Skills:** Computer Programming, Python (Programming Language), Backtesting, SAS (Software), Software Deployment, SQL Databases
- **Published:** July 13, 2026
- **Apply:** https://www.juju.com/job/00000000gg4tz8

## About the Role

+ University (Degree) Preferred, + Extensive experience in model development, testing, and production implementation across multiple asset classes, including CRE, C&I, and consumer portfolios.

 + Strong programming skills in Python, SAS, and SQL

 + Experience in model performance evaluation and back-testing

 + Strong expertise in CECL / ACL frameworks and stress testing

Preferred Qualifications

 + Master's degree and above in Economics, Finance, accounting, and related areas (highly preferred).

 + Experience with Moody's platforms

Role Specific Work Experience

 + 5+ Years Required; 7+ Years Preferred

## Description

The CECL Model Development & Implementation Lead leads Current Expected Credit Losses (CECL) model development, enhancement, and end-to-end implementation initiatives, focusing on improving model performance, expanding coverage across new segments, and ensuring models are production-ready within a scalable and automated framework., + Develops and implements Current Expected Credit Losses (CECL) models for material segments without existing coverage, including assessing portfolio characteristics and identifying key risk drivers.

 + Leads model enhancement and replacement strategy for underperforming models, including reassessment of assumptions, data inputs, and model structure.

 + Executes end-to-end model development lifecycle, including calibration, testing, documentation, validation readiness, production deployment, and model performance monitoring.

 + Applies strong understanding of cash flow-based CECL methodologies, including lifetime loss estimation, prepayment, and charge-off dynamics.

 + Identifies opportunities for system automation and process improvement across data preparation, model execution, and testing.


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