Senior Quantitative Modeler - Prepayment Models

At Fannie Mae, the inspiring work we do helps make a home a possibility for millions of homeowners and renters. Every day offers compelling opportunities to impact the future of the housing industry while being part of a collaborative team thriving in an energizing environment. Here, you will grow your career and help create access to affordable housing finance.

Job Description

As a valued colleague on our team, you will be responsible for leveraging expertise in economics, statistics (with a focus on time-series modeling), and advanced machine learning techniques (including reinforcement learning) to develop predictive models, optimize risk management strategies, and drive actionable insights. You will also conduct 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 Senior 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 the development and implementation of MBS prepayment models.
• Conduct in-depth analysis of economic indicators, market trends, and macroeconomic factors to inform modeling assumptions, parameter estimates, and scenario analysis.
• Design and implement statistical models and time series forecasting techniques to analyze historical prepayments, identify patterns, and forecast future trends.
• Apply advanced machine learning algorithms, including reinforcement learning techniques, to optimize risk management strategies, automate decision-making processes, and improve model performance.
• Collaborate closely with cross-functional teams, including data scientists, economists, risk managers, and business stakeholders, 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 TEAM

Minimum Requirements:

• 2 years of proven experience in developing and implementing predictive models.
• Bachelor’s degree in economics, Statistics, Computer Science, or a related quantitative field.

Desired Experiences:

• Advanced degree in Economics, Statistics, Computer Science, or a related quantitative field.
• Experience with time series analysis techniques and advanced machine learning algorithms in a quantitative finance or risk management environment.
• Proficiency in Python, and experience with relevant libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong understanding of econometric principles, statistical methodologies, and quantitative modeling techniques, with a focus on time series analysis, stochastic processes, and Bayesian inference.
• Experience with the MBS market.
• Excellent analytical, problem-solving, and communication skills, with the ability to translate complex technical concepts into actionable insights for non-technical stakeholders.
• Proven ability to work collaboratively in a cross-functional team environment, manage multiple projects simultaneously, and deliver high-quality results under tight deadlines.

Qualifications

Education:

Bachelor's Level Degree (Required)

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

Fannie Mae is a flexible hybrid company. We embrace flexibility for our employees to work where they choose, while also providing office space for in-person work if desired. At times, while business need may call for on-site collaboration, which means proximity within a reasonable commute to your designated office loca