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.
Essential Functions
Duties/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.
