The Fannie Mae
Lead Financial Engineer (Flexible Hybrid)
The Fannie Mae, Washington, District of Columbia, us, 20022
Full-time
Target Hiring Range (1): 119000
Target Hiring Range (2): 155000
Company Description
At Fannie Mae, futures are made. The inspiring work we do helps make a home a possibility for millions of homeowners and renters. Every day offers compelling opportunities to use tech to tackle housing’s biggest challenges and impact the future of the industry. You’ll be a part of an expert team thriving in an energizing, flexible environment. Here, you will grow your career and help create access to fair, affordable housing finance. Job Description
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.
THE IMPACT YOU WILL MAKE The Lead Financial Engineer 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 $500 billion multifamily securitization book of business. Learn and execute proprietary in-house forecasting and pricing models developed in Java, Python, and R, and analyze the results. Understand and assess 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 through proprietary tools to understand the 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. Understand multifamily loan securitizations and model their cash flows in Python and R. Perform discounted cash flow (NPV) analysis on forecasted loan structure cash flows. Understand, measure, communicate, and document modeling assumptions, output transformations, and other modeling components that 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. Qualifications
THE EXPERIENCE YOU BRING TO THE TEAM
Minimum Required Experiences: 4 years of relevant experience. Desired Experiences: MBA with quantitative analytics experience, or Master’s in Financial Engineering or Quantitative Analytics, or Ph.D. in Finance or Economics. 2 years of experience developing and running financial models or analyzing large datasets (10mm+ observations) written in Python/Java/R. AWS experience preferred. 2 years of experience communicating complex financial results to management with presentations or memos. Knowledge of SQL. 2 years of experience developing and executing cash flow models/valuation/loss forecasting for loans or securitizations (such as CMBS, CRT). Familiarity with GSE multifamily lending business, underwriting requirements, and GSE multifamily securitizations structured transactions. Skills Experience gathering accurate information to explain concepts and answer critical questions. Determining causes of operating errors and taking corrective action. Communication including communicating in writing or verbally, copywriting, planning and distributing communication, etc. Skilled in the graphical representation of information in the form of charts, diagrams, pictures, and dashboards with programs and tools such as Excel, Tableau, or Power BI. Business Insight including advising, designing business models, interpreting customer and market insights, forecasting, benchmarking, etc. Programming including coding, debugging, and using relevant programming languages. Expertise in using statistical methods, including developing and testing hypotheses, using experimental design, and running linear and logistic regressions. Skilled in cloud technologies and cloud computing. Working with people with different functional expertise respectfully and cooperatively to work toward a common goal. Tools Data analysis & Programming: SAS, Bloomberg Professional, SQL, RStudio to develop programs in R, R Language Programming, Java, Macros in Excel, Python object-oriented programming, Tableau, Amazon Web Services (AWS) offerings, development, and networking platforms, Jira. Experience with Econometrics, Statistics, Probability, Corporate Finance, Valuation, Bond Mathematics, Financial Analysis. Fannie Mae is a flexible hybrid company. We embrace flexibility for our employees to work where they choose, while also providing office space for in-person work if desired. At times, business needs may call for on-site collaboration, which means 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, 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. 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.
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At Fannie Mae, futures are made. The inspiring work we do helps make a home a possibility for millions of homeowners and renters. Every day offers compelling opportunities to use tech to tackle housing’s biggest challenges and impact the future of the industry. You’ll be a part of an expert team thriving in an energizing, flexible environment. Here, you will grow your career and help create access to fair, affordable housing finance. Job Description
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.
THE IMPACT YOU WILL MAKE The Lead Financial Engineer 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 $500 billion multifamily securitization book of business. Learn and execute proprietary in-house forecasting and pricing models developed in Java, Python, and R, and analyze the results. Understand and assess 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 through proprietary tools to understand the 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. Understand multifamily loan securitizations and model their cash flows in Python and R. Perform discounted cash flow (NPV) analysis on forecasted loan structure cash flows. Understand, measure, communicate, and document modeling assumptions, output transformations, and other modeling components that 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. Qualifications
THE EXPERIENCE YOU BRING TO THE TEAM
Minimum Required Experiences: 4 years of relevant experience. Desired Experiences: MBA with quantitative analytics experience, or Master’s in Financial Engineering or Quantitative Analytics, or Ph.D. in Finance or Economics. 2 years of experience developing and running financial models or analyzing large datasets (10mm+ observations) written in Python/Java/R. AWS experience preferred. 2 years of experience communicating complex financial results to management with presentations or memos. Knowledge of SQL. 2 years of experience developing and executing cash flow models/valuation/loss forecasting for loans or securitizations (such as CMBS, CRT). Familiarity with GSE multifamily lending business, underwriting requirements, and GSE multifamily securitizations structured transactions. Skills Experience gathering accurate information to explain concepts and answer critical questions. Determining causes of operating errors and taking corrective action. Communication including communicating in writing or verbally, copywriting, planning and distributing communication, etc. Skilled in the graphical representation of information in the form of charts, diagrams, pictures, and dashboards with programs and tools such as Excel, Tableau, or Power BI. Business Insight including advising, designing business models, interpreting customer and market insights, forecasting, benchmarking, etc. Programming including coding, debugging, and using relevant programming languages. Expertise in using statistical methods, including developing and testing hypotheses, using experimental design, and running linear and logistic regressions. Skilled in cloud technologies and cloud computing. Working with people with different functional expertise respectfully and cooperatively to work toward a common goal. Tools Data analysis & Programming: SAS, Bloomberg Professional, SQL, RStudio to develop programs in R, R Language Programming, Java, Macros in Excel, Python object-oriented programming, Tableau, Amazon Web Services (AWS) offerings, development, and networking platforms, Jira. Experience with Econometrics, Statistics, Probability, Corporate Finance, Valuation, Bond Mathematics, Financial Analysis. Fannie Mae is a flexible hybrid company. We embrace flexibility for our employees to work where they choose, while also providing office space for in-person work if desired. At times, business needs may call for on-site collaboration, which means 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, 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. 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.
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