Enterprise Analytics and Modeling

2 weeks ago

NA, United States Fannie Mae Full time
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, 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.

THE IMPACT YOU WILL MAKE
The Enterprise Analytics and Modeling - Quantitative Modeling - Lead Associate 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:

Fannie Mae seeks Quantitative Modeler – Washington DC

  • Lead projects/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 Stata, SAS, R, Python.
  • Utilize advanced data mining and/or statistical techniques to develop analytic insights, sound hypotheses, and informed recommendations.
  • Apply advanced skill, knowledge, and/or experience while leading teams to complete modeling projects aligned with established company policies and industry-wide modeling.
  • 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”.
  • Identify opportunities to apply quantitative methods to improve business performance.
  • Communicate technical subject matter clearly and concisely to department leadership and teams.
  • Coach and mentor less experienced team members.
  • This is a hybrid (work in office and at home) position.

Qualifications

THE EXPERIENCE YOU BRING TO THE TEAM

Req’d: PhD in Economics or Statistics. Coursework, research or experience modeling using Stata and R, and performing advanced economic and econometric/statistical analysis including regression, time series, panel data, discrete event, economic optimization, and stochastic and dynamic general equilibrium models.

As a condition of employment with Fannie Mae, any successful job applicant will be required to pass a pre-employment drug screen and to successfully complete a background investigation, which may also include a credit check for positions in some areas of our business.


Additional Information

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

Fannie Mae is an Equal Opportunity Employer, which means we are committed to fostering a diverse and inclusive workplace. All qualified applicants will receive consideration for employment without regard to race, religion, national origin, gender, gender identity, sexual orientation, personal appearance, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation in the application process, email us at careers_mailbox@fanniemae.com.

#NP

#LI-DNI

The hiring range for this role is set forth on each of our job postings located on Fannie Mae's Career Site. 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.

PandoLogic. Category:Science, Keywords:Quantitative Modeler, Location:WASHINGTON, DC-20402

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