Quantitative Analytics Senior (Model Risk Audit)

Freddie Mac•McLean, VA

8d•$126,000 - $190,000

About The Position

The Model Audit team in Internal Audit seeks an experienced quantitative professional to provide independent assurance over models used in significant risk management, valuation, hedging, capital, and business decisions. The role applies quantitative expertise across market risk, capital, credit risk, and AI/ML models while contributing to model governance and communicating insights to senior stakeholders. It emphasizes independent quantitative assessment of model design and outcomes through data-driven analysis, statistical evaluation, and review of quantitative results. The team independently assesses material financial, capital, market, credit, and AI/ML models that support critical business and risk management decisions. The successful candidate will participate in model audit projects, conduct quantitative analyses, evaluate model validation and oversight, document findings, and collaborate with business, IT, and AI auditors. The position is full-time and eligible for an annual incentive program. The annualized market-based salary range is $126,000–$190,000.

Requirements

Master’s degree with at least 3 years of relevant experience, or a doctorate degree in quantitative finance, economics, statistics, or a related quantitative field.
Coursework or work experience writing statistical and/or optimization programs to develop models and algorithms.
Strong quantitative reasoning, critical thinking, and intellectual curiosity.
Ability to explain complex modeling concepts clearly to technical and nontechnical audiences.
Collaborative approach to delivering risk-based audits across multiple projects.
Commitment to learning Internal Audit methodology, model governance, AI/ML techniques, and Freddie Mac’s business.
Ability to connect technical model concerns to business use, risk, and potential impact.
Experience using generative AI to enhance productivity, analyze complex documents or code, or develop analytics and automation.

Nice To Haves

Research, development, validation, or audit experience involving market risk or capital models, including interest rate, prepayment, hedging, derivative valuation, stress testing, or economic and regulatory capital models.
Experience as a quantitative analyst or researcher within a trading desk, market risk function, model development team, model validation function, or similar quantitative environment.
Programming experience using languages such as Python, R, SQL, or Java.
Experience with large, complex financial institutions and/or Big 4 internal or external audit experience.
Possession of, or willingness to pursue, professional certifications such as CIA, CPA, CISA, Data Science, AI, or Accredited Mortgage Professional (AMP).
Willingness and interest to learn how to audit deep learning, generative AI, consumer, commercial, and counterparty credit risk models.

Responsibilities

Evaluate the conceptual soundness, implementation, use, and performance of market, capital, credit, counterparty, and AI/ML models, including models for interest rate risk, prepayment, hedging, derivative valuation, stress testing, and economic and regulatory capital management.
Perform model audits and independent quantitative analyses, test reported model issues, and assess whether corrective actions address underlying model and business risks.
Evaluate the quality and effectiveness of second-line model validation and oversight.
Document testing results in work papers, identify model issues where applicable, and communicate audit conclusions with Quantitative Technical Leads, Directors, and Officers consistent with Internal Audit methodology, policies, and procedures.
Participate in audit entrance, status, findings, and exit meetings with senior management, executive stakeholders, other audit teams, and auditees.
Collaborate with business-area, IT, and AI auditors to enhance assurance over interrelated risks, reduce siloed thinking, and provide greater insight and foresight.
Use generative AI, analytics, and automation to improve audit quality and efficiency.