Lead 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 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,  macroeconomic models, including financial valuation of finance assets and derivatives, and economic capital. Additionally, you will coach and mentor team members.

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 SAS, SQL, R, or Python.
• 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.

We have one opening for this position and it can be based in our Washington, DC and Reston, Virginia office.

THE EXPERIENCE YOU BRING TO THE TABLE

Minimum Required Experiences

• 4 years of research or industry experience in quantitative finance, econometric modeling, economics, credit risk modeling or big data analytics using Python and SQL.
• Bachelor degree or equivalent

Desired Experiences

• Advanced Degree in Data Science, Economics, Math, Statistics, or a related field
• In-depth knowledge of the mortgage industry and MBS market.
• Hands-on experience building MBS prepayment models for pricing MBS and calculating risk analytics.
• Proficient in Python and skilled in AWS tools.
• Strong business acumen with the ability to digest complex issues and communicate in a concise manner to influence desired outcomes.
• Excellent communication skills with proven ability to explain complex models to business users and stakeholders.
• Experience mentoring and leading junior analysts and quantitative modelers.

Enterprise Analytics and Modeling - Quantitative Modeling - Lead Associate
Target Pay Range: $138,000 - $180,000 a year
#LI-Hybrid #LI-ME1

Qualifications

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.

Fannie Mae is an equal opportunity employer and considers qualified applicants for employment without regard to race, color, religion, sex, national origin, disability, age, sexual orientation, gender identity/gender expression, marital or parental status, or any other protected factor. Fannie Mae is committed to providing reasonable accommodations to qualified individuals with disabilities who are employees or applicants for em