Risk Analyst III, CCAR​/CECL Model Validation; Remote

Overview

This is a remote role that may be hired in several markets across the United States. This position is within First Citizens Bank's Model Risk Management (MRM) team and plays a critical role in ensuring the integrity, reliability, and regulatory compliance of the bank's credit risk models. The primary focus is on the independent validation of models used for CCAR (Comprehensive Capital Analysis and Review) stress testing and CECL (Current Expected Credit Loss) frameworks, both of which are essential components of the bank's risk management and capital planning processes.

The models supported by this role span a broad range of lending portfolios, including Residential Mortgages, Credit Cards, Auto Loans, Home Equity Lines of Credit (HELOC), Commercial & Industrial (C&I) Loans, Commercial Real Estate (CRE), Equipment Finance, and Innovation Economy segments. The analyst will collaborate with validation manager and director to conduct independent model validations for one or more specific areas based on their background and expertise.

This role provides an excellent opportunity for the analyst to rotate within the team, gaining a comprehensive and holistic understanding of CCAR stress testing and CECL frameworks.

• Comprehensive Model Validation:
  • Model Inputs Analysis: Apply data analysis techniques to assess the quality, integrity, and appropriateness of data used in the models. Examine data extraction, cleaning, transformation processes, and evaluate data‑related assumptions and limitations.
  • Model Framework Evaluation: Scrutinize the model design and construction, verifying the suitability of the modeling framework and theory for the intended use. Review model segmentation, variable selection, model testing procedures, and evaluation model assumptions, limitations, and risks.
  • Model Code Review: Review model code to ensure correctness, accuracy, and absence of material errors. Collaborate with model owners to address any identified issues.
  • Outcomes Analysis: Assess both in‑sample and out‑of‑sample back‑test results; evaluate sensitivity and scenario testing, stress testing, and quantitative and business performance metrics.
  • Benchmark Model Development: Develop models using alternative approaches to assess the performance of the champion model against benchmark models.
• Risk Identification and Mitigation: Provide effective challenges and identify potential model risks. Recommend appropriate mitigation measures and enhancements to improve model quality and compliance with regulatory standards.
• Documentation and Reporting: Produce high‑quality, comprehensive validation reports that clearly communicate findings, recommendations, and potential risks to both technical and non‑technical stakeholders. Ensure that validation documentation adheres to internal standards.
• Audit and Regulatory Review Support: Assist model validation manager in gathering and providing materials requested by internal audit and regulators, drafting responses to questions, and defending validations in exams.
• Continuous Learning and Improvement: Stay up‑to‑date with emerging trends and best practices in model validation and regulatory requirements. Contribute to the enhancement of the model validation framework by suggesting process improvements and implementing industry‑leading methodologies.

Bachelor's Degree and 2 years of experience in Risk Analytics or Analytics OR High School Diploma or GED and 6 years of experience in Risk Analytics or Analytics.

Preferred Qualifications:

• Advanced degree (Master's or Ph.D.) in a quantitative field (e.g., Statistics, Data Science, Economics, Applied Mathematics, Engineering, or a related discipline).
• This position is open to candidates at both junior and senior levels, depending on experience. Applicants with 2+ years of relevant experience may be considered for the Risk Analyst III role, while those with 5+ years of experience may qualify for the Senior Risk Analyst position.
• Proficiency in modeling techniques (e.g., linear regression, logistic regression, survival analysis, time‑series model, machine learning) and programming languages (e.g., SAS, Python, R, SQL).
• St…