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Specialized credit modeling reduces applicant density despite metro location and reputable employer.
High — requires domain-specific credit risk experience and regulatory model governance, limiting cross-industry portability.
High — explicit 5+ years in consumer lending, mandatory SAS/Python/PySpark, ML techniques, and US model governance knowledge.
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Manage the lifecycle of Acquisition Credit models including development, evaluation, validation, monitoring, and retirement.
Own model performance assessment as single point of contact for Risk Management team and lead annual model review and revalidation activities.
Lead automation projects for reporting and data extraction using SAS, Python, Pyspark, Tableau, and provide analytic support on business initiatives.
Bachelor's degree in quantitative discipline (Risk, Economics, Finance, Mathematics, Statistics, Engineering) with 5+ years in analytical/quantitative role in consumer lending or 7+ years relevant experience without degree.
Proficiency in SAS, SQL, Python/Pyspark, and reporting tools like Tableau.
Strong knowledge of statistical and machine learning techniques (logistic regression, Random Forest, XGBoost).
Work Experience Required: Minimum 5+ years in consumer lending analytical/quantitative roles.
Experienced in end-to-end credit model development and governance with exposure to US model governance standards and credit policies.
Ability to manage multiple projects independently with strong analytical problem-solving and communication skills.
Familiar with credit bureau data and capable of leading automation and mentoring junior team members.