Enterprise Modeling and Analytics - Quantitative Engineering - Senior Associate

Fannie Mae • Reston, VA

8d • $123,000 - $161,000 • Hybrid

About The Position

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. Job Description As a valued colleague on our team, you will, under limited supervision design, implement, and maintain high performance analytical and modeling systems that power Multifamily financial reporting, pricing, capital, and portfolio analytics. This role blends advanced financial engineering, econometric modeling, and engineering to deliver production grade platforms used across the enterprise. The ideal candidate brings deep quantitative expertise, strong engineering discipline, and a passion for modernizing legacy modeling environments. You will collaborate closely with quantitative modelers, data engineering teams, and business partners to implement models, streamline analytical workflows, and enhance the computational backbone of Multifamily Credit Works.

Requirements

• 2 years plus experience designing and deploying quantitative software solutions in Python, R and SQL, Bachelor degree or equivalent
• Ability to translate complex mathematical, financial, and business logic into production quality software.
• Strong experience developing thorough test frameworks to validate modeling and analytical requirements.
• Experience with econometrics, logistic regression, Monte Carlo simulations, asset pricing methods, and detailed cash flow analytics.
• Working knowledge of mortgage finance, structured data ecosystems, analytical platforms, and regulatory frameworks such as capital rules and stress testing.
• Excellent written and verbal communication skills, with the ability to collaborate effectively across engineering teams, quantitative groups, business partners, and senior leadership.

Nice To Haves

• Masters or higher in Computer Science, Engineering, Applied Mathematics, Statistics, Economics, Finance, or a related quantitative discipline, with 6+ years of relevant experience.
• Proficiency working in AWS and familiarity with Java is a plus.
• Multifamily mortgage expertise strongly preferred.
• Hands-on experience using GenAI development tools (e.g., GitHub Copilot, LLM based assistants) to accelerate coding workflows, improve documentation, automate refactoring, and reinforce engineering standards.
• Proficient with Agile methodologies and tools such as Jira for task management and sprint planning.

Responsibilities

• Implement, test, and optimize components of Multifamily integrators, econometric models, and statistical forecasting frameworks using financial engineering and data science methodologies.
• Modernize Python and R modeling applications, applying GenAI coding tools and prompt engineering techniques to migrate and refactor components into Python for improved performance, clarity, and maintainability.
• Design, engineer, and optimize analytical and modeling systems, performing detailed cash flow and scenario analytics to support business insights and decision making
• Translate complex mathematical and business logic into reliable, scalable production code; develop and execute comprehensive unit and integration tests to ensure model integrity.
• Partner with model development, data engineering, and business stakeholders to deploy analytics solutions aligned with modeling requirements, regulatory needs, and operational workflows.
• Contribute to engineering best practices, documentation standards, and Agile delivery processes.

Benefits

• 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.

What This Job Offers

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