Lead Quantitative Modeler - Prepayment Models

Fannie Mae • Washington, DC

7d • $141,000 - $184,000 • Onsite

About The Position

Fannie Mae is hiring a Lead Quantitative Modeler to lead theoretical and empirical research using public and proprietary data across mortgage finance. The role applies expertise in economics, statistics—with a focus on time-series modeling—and advanced machine learning to develop predictive models, improve risk management strategies, and generate actionable insights. The position also coaches and mentors team members and leads teams conducting quantitative analysis, modeling, and programming.

The work spans mortgage products and securities, borrower behavior, investment and hedging strategies, residential property valuation, macroeconomic models, financial valuation of financial assets and derivatives, economic capital, and stress testing. A particular focus is supporting the development and implementation of retained portfolio whole-loan behavioral and cash-flow models for non-performing and re-performing loans, as well as mortgage-backed securities prepayment models.

The modeler develops and evaluates loan-level datasets and performs variable selection, calibration, backtesting, benchmarking, sensitivity testing, and model performance analysis. The role translates behavioral model outputs into cash flows and assesses valuation and risk measures, including duration, key-rate duration, and convexity. It also applies AI/ML-enabled tools where appropriate, collaborates with business and technical stakeholders, and maintains model documentation and code repositories in compliance with regulatory requirements and internal policies.

The position is generally expected to work onsite regularly at the designated office location; the work cadence is determined by the manager. The role has a stated hiring range of $141,000 to $184,000 per year and is eligible for participation in a Fannie Mae incentive program, subject to its terms.

Requirements

• At least 4 years of experience developing and implementing agency MBS prepayment, transition/termination, survival or time-to-event, multinomial/logistic, or other credit-risk and behavioral models.
• Strong understanding of statistical modeling, model calibration, backtesting, benchmarking, sensitivity analysis, scenario analysis, and stress testing.
• Strong written and verbal communication skills, including the ability to explain technical concepts to quantitative and nontechnical audiences.
• Advanced programming skills, particularly in Python, and ability to develop scalable analytical solutions.
• Curiosity and adaptability in learning and responsibly applying new technologies, including artificial intelligence.
• Bachelor’s degree or equivalent in Economics, Statistics, Computer Science, or a related quantitative field; the posting lists a bachelor’s-level degree as required.

Nice To Haves

• Advanced degree preferred.
• Strong understanding of mortgage borrower behavior and credit performance, including delinquency progression, cure/re-default, modification/forbearance, prepayment, liquidation, recovery timing, and loss severity.
• Strong understanding of fixed-income valuation and mortgage cash-flow modeling, including discounted cash flow, interest-rate sensitivity, duration, convexity, and related risk analytics.
• Experience with Python libraries and frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Experience building user interfaces for analytics tools in an AWS environment.
• Strong knowledge of AI/ML and experience with AI/ML models for mortgage and loan modeling.
• Experience with AI workflows for quantitative modeling and/or software engineering.
• Ability to collaborate with Capital Markets, model users, risk, technology, and governance teams; manage multiple projects; and deliver high-quality results under tight deadlines.

Responsibilities

• Lead teams in developing practical solutions that support business strategies and initiatives, and conduct or assess ad hoc quantitative analyses, modeling, and programming using Python, SQL, R, or SAS.
• Coach and mentor team members in using data-mining and statistical techniques to develop analytic insights, hypotheses, and recommendations.
• Identify opportunities to apply quantitative methods to improve business performance.
• Lead modeling projects aligned with company policies and industry modeling practices.
• Contribute to model validation and testing strategies; assess model methodologies, outputs, processes, and risks; interpret results in business context and monitor performance.
• Communicate technical subject matter clearly to department leadership and teams.
• Analyze economic indicators, market trends, borrower behavior, and macroeconomic factors to inform modeling assumptions, parameter estimates, cash-flow forecasts, and scenario analysis.
• Support the development and implementation of retained portfolio whole-loan behavioral and cash-flow models, particularly for non-performing and re-performing loans.
• Build and evaluate model-ready loan-level datasets; perform variable selection, calibration, in-sample and out-of-sample backtesting, benchmarking, sensitivity testing, and performance analysis.
• Translate behavioral model outputs into cash flows and assess valuation and risk measures such as duration, key-rate duration, and convexity; reconcile and benchmark results across models and implementations.
• Support the development and implementation of MBS prepayment models.
• Use AI/ML-enabled tools and advanced analytical techniques where appropriate to accelerate model research, coding, testing, documentation, and implementation while maintaining controls and model governance.
• Collaborate with Capital Markets stakeholders, data scientists, economists, risk managers, and other cross-functional partners to gather requirements, define scope, and drive model development.
• Develop and maintain documentation, code repositories, and model validation reports in compliance with regulatory requirements and internal policies.
• Stay current with developments in quantitative finance, econometrics, statistics, and machine learning, and apply relevant methods to enhance modeling capabilities.

Benefits

• Participation in a Fannie Mae incentive program, subject to program terms.
• A broad benefits package covering health, life, voluntary lifestyle, and other benefits and perks intended to support physical, mental, emotional, and financial well-being.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior