Senior Quantitative Modeler - (Hybrid)

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, under limited supervision, conduct theoretical and empirical research with public and proprietary data in all areas of mortgage finance business, including 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.

THE IMPACT YOU WILL MAKE
Senior Quantitative Modeler 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:

• Apply moderately complex mathematical, statistical, and econometric techniques to provide innovative, thorough, and practical solutions that support business strategies and initiatives. Conduct or assess 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.
• Apply experience and skill to complete modeling projects aligned with established company policies and industry-wide modeling practices.
• Implement validation or testing strategies and assess the quality and risk of model methodologies, outputs, and processes. Apply understanding of relevant business context to interpret model results, monitor performance, and assess risks.
• Communicate complex technical subject matter clearly and concisely, both verbally and through written communication, such as white papers, review reports, or workpapers.

Qualifications

THE EXPERIENCE YOU BRING TO THE TEAM

Minimum Required Experience:

• Demonstrated academic training or empirical experience on econometrics or statistics, including data preparation, data analytics and model estimation.
• Ability to provide customer support in the areas of model forecast and analytics.
• Ability to assess model implementation quality and production control risk with some supervision.
• Ability to work in a team environment, establish and maintain relationships within and across division.
• Demonstrated experience using some of the programming languages commonly used in model and application development.
• Bachelor’s Degree or equivalent required.
• 2 years of related experience.

Desired Experience:

• Demonstrate experience in model development and model implementation for forecasting, working with modelers and business users.
• Ability to conduct research, model development, model performance tracking and enhance models to meet specific analytical needs.
• Demonstrate experience in Machine Learning-based modeling; Hands-on experience with popular ML frameworks and libraries.
• Ability to review technical requirements, provide prototype code, and test results to assist application development team in implementing new/updated models.
• Proficient in SAS, knowledge of relational Databases/SQL, experienced with R/Python.
• Solid programming skills including writing programs from scratch and improving and debugging existing programs.
• Effective communication skills, including the ability to communicate technical results to non-technical audience.
• Advanced degree in Statistics, Economics Finance, or another related field.

Skills

• Expertise in using statistical methods for data analysis, model development and hypothesis testing, including estimating linear and logistic regressions.
• Solid programming skills including writing programs from scratch and improving and debugging existing programs.
• Business insight including designing business models to address customers’ requirements, interpreting customer and market insights, model forecasting results and benchmarking analysis.

Tools
Experience using SAS and SQL
Skilled in R/python

Additional Information

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