Sr Statistical Modeling Analyst
$128,900–$157,500 year
RemoteWashington, United States
Job Summary
Develop and calibrate statistical models for Probability of Default, Loss Given Default, and Exposure at Default using SAS, Python, SQL, and R. Support documentation and execution of credit risk models for loan originations, loss forecasting, and capital planning. Collaborate with business partners to interpret model results and assess statistical methods for generating actionable insights. Participate in annual model reviews, performance testing, and data request processes while maintaining thorough documentation. Deliver regular reports on modeling impacts and portfolio trends to assist in credit risk strategy development.
Required Qualifications
- Master's degree or foreign equivalent in a quantitative discipline such as statistics, math, finance, or economics
- Coursework in statistics at either the bachelor's, master's or PhD level
- Minimum 3 years of functional experience in statistical modeling
- Credit risk modeling experience in one or more of the following product areas: real estate secured loan products (mortgage, home equity), auto, credit card or commercial loan products
- Sound knowledge of statistical modeling concepts, including logistic regression, survival analysis, Markov chain analysis and time series methodologies
- Experience developing and validating Probability of Default (PD), Exposure at Default (EAD), and Loss Given Default (LGD) models
- 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
- Ability to interact with management officials at all levels, as well as other risk and model management personnel throughout the Credit Union
- Ability to analyze and reconcile large volume of data so that it can be summarized and eventually used for management decisions
Desired Qualifications
- Experience with statistical modeling for capital planning and stress testing
- Experience with Comprehensive Capital Analysis Review (CCAR), Dodd-Frank Act Stress Testing (DFAST) and Basel Regulatory Capital Framework
- Experience with modelling techniques including logistic regression, multivariate analysis, and Monte Carlo
- Excellent analytical and problem-solving skills
- Experience in verbal and written communication of complex statistical insights and implications to Credit Union strategy and value creation
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