05 Apr
Financial Engineering Associate
District of Columbia, Washington , 20001 Washington USA

Job Description

As a valued colleague on our team, you will assist with supporting the team in applying mathematical models, advanced tools or techniques (such as SAS, Python, and R), and financial industry knowledge to business or financial data, including model results. Your efforts will enable the team to analyze or report on business performance, solve business questions, or inform business decisions. Work may include developing models or prototypes to achieve these goals, but is not the core focus in the role.

THE IMPACT YOU WILL MAKEThe Multifamily - Financial Engineering – 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:

  • Join and work with a team of several analysts to assess and monitor credit risk on Fannie Mae’s $400bln + multifamily securitization book of business.
  • Learn and execute proprietary in-house forecasting and pricing models developed in java, Python and R, and analyzing the results. Analyze loan level results provided by off the shelf forecasting application, typically thru tools such as Excel Pivot Tables, R, Python
  • Understanding and assessing the upstream input data including MF loan data flows, transformations, and how changes to the upstream data drive changes in credit forecasts
  • Generate forecasts of property revenues, property prices and macroeconomic variables using production applications. Analyze monthly and quarterly changes in forecast, recent history. Synthesize findings in existing reports and share with users
  • Participate with a team in developing, executing, validating, and documenting impact on loan risk and return metrics from periodic updates to models or applications
  • Generate, validate, share findings, and upload data from monthly or quarterly analyses. These production processes are used by our regulator, asset management, counterparty risk management teams
  • Develop understanding of Multifamily loans and model their cash flows in Python and R. Perform discounted cash flow (NPV) analysis on forecasted loan structure cashflows
  • Understand, measure, communicate and document sensitivities or attributions for model outputs, data transformations, and other modeling components drive the results of various analyses

Qualifications

THE EXPERIENCE YOU BRING TO THE TEAM

Desired Experiences

  • Bachelor degree or equivalent in a quantitative field

Additional Information

Job ID: REF10260G

In response to COVID-19, Fannie Mae has adapted ourworkplace and hiring processesto better safeguard our employees, candidates, and new hires.We understand that this is an unprecedented situation and Fannie Mae is committed to creating protocols for these processes that are agile and conform with federal, state, and local health administration guidance. While the company's operating status for on-site work is currently voluntary, the majority of Fannie Mae's workforce is remote until further notice. We continue to conduct all interviews and onboarding virtually. In addition, all employees who wish to come on site must be fully vaccinated against COVID-19 and enter their vaccination information into a confidential HR system before arriving at the facility, unless they have an approved accommodation.Click hereto go directly to information about accommodations.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 [emailprotected]


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