Statistical Modeling Manager

Join to apply for the Statistical Modeling Manager role at BECU
. This is a people-first financial institution with 1.5 million members and over $30 billion in managed assets. At BECU, success comes from focusing on people, not profits. You’ll be part of a purpose‑driven organization that serves members, communities and each other.

The target pay range for this position is $–$ annually. The full pay range is $–$ annually. Compensation decisions are determined using relevant job‑related skills, experience, education or training, and individual qualifications.

• 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

As a Statistical Modeling Manager you’ll lead the development and oversight of advanced credit risk models that shape how we approach economic capital, loan loss forecasting, account management, collections, capital planning and stress testing. Your expertise will translate data into actionable insights and strengthen the financial future of the people and communities we serve.

• Lead model development: design, develop, and recalibrate statistical credit risk models, such as credit decision scorecards and Basel IRB models (PD, LGD, EAD) using leading statistical software and programming tools.
• Champion data integrity: gather, validate and refine large datasets, applying 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 performance assessments and annual reviews to improve model accuracy cutting‑edge statistical methods.
• Drive business alignment: partner with business and product teams to explain model outcomes and guide risk‑reward strategies.
• Provide advanced analytics in support of credit risk strategy, including capital planning, portfolio mix management and loss forecasting using SAS, SQL and other statistical platforms.
• Standardize model governance: develop and maintain risk modeling procedures and documentation for consistency, auditability and stakeholder transparency.
• Translate insights: present model results and recommendations clearly to technical and non‑technical stakeholders.
• Stay ahead of the curve: maintain up‑to‑date knowledge of credit portfolios, regulatory requirements and industry trends.
• Deliver cross‑functional support: respond to data requests, manage testing environments and ensure model outputs are leveraged effectively across teams.
• Maintain thorough documentation: keep detailed records including model development logs, version controls and validation documentation.
• Contribute beyond the role: take on additional responsibilities and special projects that support BECU’s mission and modeling excellence.

• Master’s degree or foreign equivalent in a quantitative discipline such as statistics, math, finance, or economics.
• Minimum seven years of functional experience in credit risk modeling.

• 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.
• Proficiency in at least three of SAS, Python, SQL and R.
• Experience communicating complex statistical insights.
• Experience with loss forecasting, default management and credit risk modeling, reporting and analytics.
• Experience with Basel regulatory framework, CCAR and DFAST.
• Experience with real‑estate secured loan products, auto, credit card, consumer and/or commercial loan products.

Ready to make an indelible impact? We invite you to apply, bring your expertise to the table and help us grow together. Your journey of influence, innovation and impactful contribution starts now.

BECU is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, veteran status, disability, sexual orientation, gender identity or any other protected status.