Lead Quantitative Modeler - Prepayment Models

About the role

Playing an essential role in the U.S. economy, Fannie Mae is foundational to housing finance. Here, your expertise can help fuel purpose-driven innovation that expands access to homeownership and affordable rental housing across the country. Join Fannie Mae to grow your career and help people find a place to call home.

About the role

As a valued colleague on our team, you will act as team lead while conducting theoretical and empirical research with public and proprietary data in all areas of the mortgage finance business.

This may include mortgage products and securities, borrower behavior, investment and hedging strategies, residential property valuation, macroeconomic models, including housing prices and interest rate, financial valuation of finance assets and derivatives, economic capital, and stress testing. Additionally, you will coach and mentor team members.

You will be responsible for leveraging expertise in economics, statistics (with a focus on time-series modeling), and advanced machine learning to develop predictive models, optimize risk management strategies, and drive actionable insights. You will also act as team lead while conducting theoretical and empirical research with public and proprietary data in all areas of the mortgage finance business.

This may include mortgage products and securities, borrower behavior, investment and hedging strategies, residential property valuation, macroeconomic models, including housing prices and interest rate, financial valuation of finance assets and derivatives, economic capital, and stress testing.

The Impact You Will Make

The Lead Quantitative Modeler - Prepayment Models role will offer you the flexibility to make each day your own, while working alongside people who care so that you can deliver on the following responsibilities:

• Lead teams in providing innovative, thorough, and practical solutions that support business strategies and initiatives and in conducting or assessing ad hoc quantitative analyses, modeling, or programming using Python, SQL, R, or SAS.
• Coach and mentor team members in utilizing data mining and/or statistical techniques to develop analytic insights, sound hypotheses, and informed recommendations. Identify opportunities to apply quantitative methods to improve business performance.
• Apply advanced skill, knowledge, and/or experience while leading teams to complete modeling projects aligned with established company policies and industry-wide modeling practices.
• Contribute to developing validation or testing strategies and assessing the quality and risk of model methodologies, outputs, and processes and applying understanding of relevant business context to interpret model results, monitor performance, and assess risks.
• Communicate technical subject matter clearly and concisely to department leadership and teams.
• Conduct in-depth analysis of 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, with emphasis on non-performing (NPL) and re-performing (RPL) loans.
• Build and evaluate model-ready loan-level datasets and perform variable selection, calibration, in-sample/out-of-sample backtesting, benchmarking, sensitivity testing, and model performance analysis.
• Translate behavioral model outputs into cash flows and assess impacts on 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.
• Apply AI/ML-enabled tools and advanced analytical techniques, where appropriate, to accelerate model research, coding, testing, documentation, and implementation while maintaining appropriate controls and model governance.
• Collaborate closely with cross-functional teams, including business stakeholders in Capital Markets, data scientists, economists, and risk managers to gather requirements, define project scope, and drive model development efforts.
• Develop and maintain documentation, code repositories, and model validation reports in compliance with regulatory requirements and internal policies.
• Stay current with emerging trends, methodologies, and best practices in quantitative finance, econometrics, statistics, and machine learning, and apply them to enhance modeling capabilities and stay ahead of the curve.

The Experience You Bring to the Table

Minimum Required Experiences

• 4 years of proven experience in 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 both quantitative and nontechnical audiences.
• Advanced programming skills, particularly in Python, with the ability to develop scalable analytical solutions.
• Shows curiosity and adaptability in learning and responsibly applying new technologies, including artificial intelligence, to reimagine how we work.

Desired Experiences

• Bachelor degree or equivalent in Economics, Statistics, Computer Science, or a related quantitative field. 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.
• Strong programming skills in Python, experience with relevant libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Experience in building UI for analytics tools in AWS environment a strong plus.
• Strong knowledge in AI/ML and experienced with AI/ML models for mortgage and loan modeling.
• Experienced with AI workflows for quantitative modeling and/or software engineering.
• Proven ability to collaborate with Capital Markets, model users, risk, technology, and governance teams; manage multiple projects and deliver high-quality results under tight deadlines.

Target Pay Range

$141,000.00 - 184,000.00 a year

Internal Job Title: Enterprise Modeling and Analytics - Quantitative Modeling - Lead Associate

Qualifications

Credit Risk Modeling, Data Analytics, Machine Learning (ML), Mortgage Analytics, Prepayment, Python (Programming Language)

Education: Bachelor's Level Degree (Required)

The future is what you make it to be. Discover compelling opportunities at Fanniemae.com/careers.

• For most roles, employees are expected to work onsite on a regular basis at their designated office location.
• In-office work cadence is determined by your manager.
• Proximity within a reasonable commute to your designated office location is preferred unless the job is noted as open to remote.

Pay and benefits

The hiring range for this role is set forth below. Final salaries will generally vary within that range based on factors that include but are not limited to, skill set, depth of experience, certifications, and other relevant qualifications. This position is eligible to participate in a Fannie Mae incentive program (subject to the terms of the program). As part of our comprehensive benefits package, Fannie Mae offers a broad range of Health, Life, Voluntary Lifestyle, and other benefits and perks that enhance an employee's physical, mental, emotional, and financial well-being. See more here.

Requisition compensation: 141000 to

184000