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Tier-1 bank, metro location and mid-level analyst role increases candidate density despite specialized risk modeling skillset.
Requires CCAR/DFAST/CECL regulatory modeling and secured-lending econometrics, limiting cross-industry transferability.
Requires regulatory risk modeling expertise, econometrics, Python/SAS skills and Master's, making filters strict.
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Develop CCAR/DFAST/Climate risk stress loss models for secured portfolios such as Home Equity and Mortgage.
Manage end-to-end model development lifecycle including data QA/QC, modeling, validation/recalibration, testing, documentation, and regulatory presentations.
Collaborate with cross-functional teams including business stakeholders, model validation, governance, and implementation teams to support regulatory stress testing requirements.
Advanced degree (Masters preferred) in Statistics, Applied Mathematics, Operations Research, Economics, Quantitative Finance, or related field.
2+ years of analytic experience with emphasis on quantitative analysis, statistical modeling, and econometric modeling of consumer credit risk stress losses.
Strong programming skills in Python, SAS, and AI automation workflows; experience in regression, time series, and optimization modeling.
Work Experience Required: 2+ years in quantitative risk modeling; experience specifically in secured lending product regression models and AI-driven automation preferred.
Demonstrated experience in developing and validating regulatory stress loss models (CCAR/DFAST/CECL/Climate risk) for secured credit portfolios.
Strong technical proficiency to independently manage complex quantitative modeling tasks and automation of workflows with moderate supervision.
Ability to communicate technical concepts effectively to both technical and non-technical stakeholders including regulatory agencies.