Lead Model Development: Design, develop, and recalibrate statistical credit risk models—ranging from credit decision scorecards to Basel IRB models like PD, LGD, and EAD—using leading statistical software and programming tools.
Champion Data Integrity: Gather, validate, and refine large datasets to ensure models are built on reliable, usable data—and apply advanced treatment techniques where needed.
Implement with Precision: Manage systems testing and data readiness to support accurate and efficient model implementation.
Evaluate and Enhance Models: Conduct ongoing performance assessments and annual reviews to identify enhancements and improve model accuracy using cutting-edge statistical methods.
Drive Business Alignment: Partner with business and product teams to explain model outcomes, guide risk-reward strategies, and ensure alignment between statistical insights and business objectives.
Maximize Analytic Impact: Provide advanced analytics in support of credit risk strategy, including capital planning, portfolio mix management, and loss forecasting—applying tools like SAS, SQL, and other statistical platforms.
Standardize Model Governance: Develop and maintain risk modeling procedures and documentation to support consistency, auditability, and stakeholder transparency.
Translate Insights: Present model results and recommendations clearly to both technical and non-technical stakeholders, supporting enterprise-wide understanding and action.
Stay Ahead of the Curve: Maintain up-to-date knowledge of credit portfolios, regulatory requirements, and industry trends to drive continuous improvement in modeling practices.
Deliver Cross-Functional Support: Respond to data requests, manage testing environments, and ensure model outputs are leveraged effectively across teams.
Ensure Thorough Documentation: Maintain detailed records, including model development logs, version controls, and validation documentation for regulatory and business needs.
Contribute Beyond the Role: Take on additional responsibilities and special projects that support BECU’s mission and modeling excellence.

Requirements

Master’s degree or foreign equivalent in a quantitative discipline such as statistics, math, finance, or economics.
Minimum 7 years of functional experience in credit risk modeling.
Sound knowledge of statistical modeling concepts including logistic regression, survival analysis, Markov chain analysis and time series.
Knowledge of artificial intelligence (AI) and machine learning (ML) tools.
Knowledge of three or more of the following statistical analytical packages: SAS, Python, SQL and R.
Experience in verbal and written communication of complex statistical insights.
Experience with loss forecasting, default management and credit risk modeling, reporting and analytics.
Experience with Basel Regulatory framework, Comprehensive Capital Analysis Review (CCAR), Dodd-Frank Act Stress Testing (DFAST).
Credit Risk modeling experience in real estate secured loan products (i.e., mortgage, home equity), auto, credit card, Consumer and/or commercial loan products.

Benefits

401(k) Company Match (up to 3%)
4% annual contribution to your 401(k) by BECU
Medical, Dental and Vision (family contributions as well)
PTO Program + Exchange Program
Tuition Reimbursement Program
BECU Cares volunteer time off + donation match

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

statistical credit risk modelscredit decision scorecardsBasel IRB modelslogistic regressionsurvival analysisMarkov chain analysistime seriesloss forecastingdefault managementcredit risk modeling

Soft Skills

communicationdata validationmodel evaluationbusiness alignmentstakeholder engagementcross-functional supportdocumentationanalytical thinkingproblem-solvingteam collaboration