Senior Credit Risk/Decision Scientist

Overview

POSITION DESCRIPTION

Title: Senior Credit Risk/Decision Scientist Classification: Salaried, exempt Position Type: Full Time Reports to: Credit Risk Officer – Digital Banking Location: TBD Summary / Objective The Senior Credit Risk/Decision Scientist will be responsible for quantitative model development, credit strategy design, and analytical decision support throughout the customer life cycle. This role will build, validate, and monitor predictive models; design and interpret strategy tests; and translate analytical findings into actionable credit policy. Responsibilities Develop, validate, and recalibrate credit risk scorecards and predictive models for acquisition, account management, and loss forecasting. Design and analyze champion-challenger tests to optimize credit policy and decisioning thresholds. Partner with Marketing to enhance response and bidding models focused on improved conversion and acquisition cost Monitor model performance through ongoing back-testing, stability analysis, and drift detection; recommend recalibration as needed. Integrate and evaluate third-party data vendors to enhance model features, leads waterfall and risk segmentation. Support prescreen modeling and strategies in partnership with marketing and credit strategy teams. Conduct portfolio-level risk analysis including delinquency trending, vintage analysis, and loss projections. Collaborate with compliance on model risk governance, fair lending review, and SR 11-7 documentation requirements. Prepare clear model documentation, validation reports, and executive-ready presentations for internal stakeholders and regulators. Partner with IT and data engineering teams on data pipelines, feature engineering, and model deployment in production environments. Contribute to fraud detection and collections analytics as workflow allows, supporting cross-functional risk initiatives. Competencies 5+ years of experience in credit risk modeling, decision science, or quantitative analytics within a bank, credit union, fintech, or consumer lender. Demonstrated experience building and validating scorecards using logistic regression, decision trees, gradient boosting, or similar techniques. Strong proficiency in Python or R for statistical modeling, data manipulation, and visualization Solid SQL skills; ability to independently access and analyze large datasets Familiarity with credit bureau data (Experian, Equifax, TransUnion) and alternative data sources. Understanding of model risk management frameworks, including SR 11-7 / OCC 2011-12 guidance. Strong analytical communication skills — ability to translate complex model outputs into actionable business recommendations. Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Finance, or a related quantitative field. #J-18808-Ljbffr TBO Bank