Lead Financial Engineer (Flexible Hybrid)

Fannie Mae - Washington, DC

Full-time - Mid Level

Hybrid - Washington, DC

Credit Intermediation and Related Activities

About the position

As a valued colleague on our team, you will provide expert advice and guidance to the team responsible for 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.

Responsibilities

• Join and work with a team of several analysts to assess and monitor credit risk on Fannie Mae's $500 billion 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.
• Quantitatively analyze multifamily loan terms, products and securitizations through forecasts of NOI, Cap Rates, Interest Rates, Property Prices.
• Execute deterministic what-if scenarios thru proprietary tools to understand impact on MF loan book. Synthesize results and document methodology in brief memos using Monte Carlo and other simulation techniques.
• Implement code changes to modify and/or extend in-house models, or to develop new models from scratch.
• Understanding Multifamily loan securitizations 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 modeling assumptions, output transformations, and other modeling components drive the results of various analyses.
• Participate with a team in developing, executing, validating, and documenting proprietary valuation models and property price indexes. Work collaboratively with stakeholders (business, finance, risk, economists) to discuss options and arrive at a recommended approach.
• Synthesize and share with management attribution and sensitivity analysis (attributing changes in model outputs to changes in inputs and assumptions, and understanding and documenting sensitivity of model outputs to changes in inputs and assumptions).
• Perform what-if or strategic analysis to investigate how contemplated changes to loan terms might impact the financial outcomes (capital, returns, pricing) for Fannie Mae and the borrower.

Requirements

• 4 years of experience in a related field.

Nice-to-haves

• MBA with quantitative analytics experience, or Master's in Financial Engineering or Quantitative Analytics, or Ph.D. in Finance or Economics.
• 2 years experience developing and running financial models or analyzing large datasets (10mm+ observations) written in Python/Java/R. AWS experience preferred.
• 2 years experience communicating complex financial results to management with presentations or memos.
• Knowledge of SQL.
• 2 years experience developing and executing cashflow models/valuation/loss forecasting for loans or securitizations (such as CMBS, CRT).
• Familiarity of GSE multifamily lending business, underwriting requirements, and GSE multifamily securitizations structured transactions.

Benefits

• Broad range of Health, Life, Voluntary Lifestyle, and other benefits and perks that enhance an employee's physical, mental, emotional, and financial well-being.