Job Description As a Advisor in Quantitative Modeling, you will apply extensive knowledge of advanced modeling techniques, joining a modeling community which creates solutions that use public and proprietary data in all areas of the mortgage finance business including mortgage products and securities, borrower behavior, investment and hedging strategies, residential property valuation, macroeconomic models including of housing prices and interest rates, valuation of financial assets and derivatives, economic capital, and stress testing.

　　As a Advisor within the

　　Enterprise Model Risk

　　function, you will be a Model Validator ensuring that models are operating as intended, have been well-designed using sound application of statistical model development and econometric model-building practices, and are appropriate for their proposed model usages.

　　THE IMPACT YOU WILL MAKE

　　The

　　Quantitative Model Validator Advisor

　　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:

　　Contribute to the production of the Model Validation Reports through technical analysis and write-ups.

　　Recommend improved methods and techniques to provide innovative, thorough, and practical solutions that support business strategies and initiatives, as well as better ways of conducting or assessing ad hoc quantitative analyses, modeling, or programming using SAS, SQL, R, or Python.

　　Utilize advanced 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.

　　Counsel teams on company policies and industry-wide modeling practices.

　　Inform the team's technical direction for validation or testing strategies and assessment of 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 team leadership and project stakeholders.

　　Qualifications THE EXPERIENCE YOU BRING TO THE TEAM

　　Minimum Required Experiences

　　6 years

　　Bachelor degree or equivalent

　　Desired Experiences

　　An advanced degree (PhD. or Masters) in a quantitative field such as Computer Science, Statistics, Biostatistics, Economics (with an Econometrics emphasis), Applied Finance, or Applied Mathematics.

　　Experience in developing models or validating models used in credit risk or market risk.

　　Experience with a large financial institution which has Enterprise Risk Management functions that meet the needs of highly regulated financial institutions.

　　Skills

　　Fast-learning, building on a strong foundation of knowledge to continuously learn new techniques for building and managing model risks well.

　　Communicating technical subject matter clearly and concisely, both verbally and through well-written communications including Initial Assessment Reviews and Validation Reports.

　　Programming including coding, debugging, and using relevant programming languages

　　Experience in the process of analyzing data to identify trends or relationships to inform conclusions about the data

　　Expertise in using statistical methods, including: developing and testing hypotheses, using experimental design, and running linear and logistic regressions

　　Tools

　　Advanced skills in using computer languages often used in model development such as Python and R.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 [email protected].