Joseph Crouse
About Joseph Crouse
Joseph Crouse is a Data Science Lead Associate at Fannie Mae in Washington, D.C., specializing in analytics at the intersection of technology, econometric and financial modeling, and business.
Title
Joseph Crouse holds the position of Data Science Lead Associate at Fannie Mae. He specializes in data science and analytics, working with a focus on econometric and financial modeling to solve business problems. His expertise in deriving data-driven solutions facilitates this role.
Company
Joseph Crouse is currently working at Fannie Mae, a government-sponsored enterprise located in Washington, District of Columbia, United States. Fannie Mae focuses on expanding affordable housing and reducing risk for investors in the U.S. mortgage market. Joseph contributes to Fannie Mae's mission by using his data science skills to tackle financial and economic challenges.
Education and Expertise
Joseph Crouse completed his Master's degree in Applied Economics at the University of Maryland College Park from 2017 to 2018. Prior to this, he earned a Bachelor's degree in Economics from the same institution, graduating in 2016. His academic background equips him with a thorough understanding of economic theories and applications. He also attended Broadneck Senior High School from 2008 to 2012.
Background
Joseph Crouse's educational journey started at Broadneck Senior High School, from which he graduated in 2012. He then pursued higher education at the University of Maryland College Park, where he first earned a B.S. in Economics within three years. Following this, he achieved a Master's in Applied Economics. His academic focus has been primarily on economics, which lays the foundation for his current analytics and data science role at Fannie Mae.
Professional Start Date at Fannie Mae
Joseph Crouse began his role as Data Science Lead Associate at Fannie Mae on July 1, 2021. Since then, he has been leveraging his skills in data science, financial modeling, and technology to contribute to the company's mission of expanding affordable housing and providing reliable statistics and models for financial decision-making.