Javed Shaik
About Javed Shaik
Javed Shaik is a Data Scientist at Fannie Mae in Washington, D.C., with a background in data science and neuroscience.
Current Position at Fannie Mae
Javed Shaik currently works as a Data Scientist at Fannie Mae in Washington, District of Columbia, United States. In his role, he has developed a variety of analytical tools and models. These include a decision tree model to measure the impact of borrowers exiting forbearance on mortgage servicer performance and an advanced model for determining the pricing of Mortgage Servicing Rights (MSR).
Previous Role at BioInformatics Inc.
Before joining Fannie Mae, Javed Shaik worked at BioInformatics Inc. as a Data Science Engineer from 2018 to 2019. During this year, he contributed to various data science projects, enhancing the organization's ability to handle and interpret complex biological data.
Teaching Experience at George Mason University
Javed Shaik served as a Data Science Teacher Assistant at George Mason University from 2018 to 2019 in Fairfax, VA. In this academic role, he provided instruction and support to students in data science courses, helping them understand complex topics and practical applications of data science methodologies.
Educational Background
Javed Shaik pursued higher education at George Mason University, earning credentials in Computational and Data Sciences from 2017 to 2019. He also studied Neuroscience at the University of Virginia from 2015 to 2017. Earlier, he attended Thomas Jefferson High School for Science and Technology, where he focused on Neuroscience for five years, graduating in 2015.
Developed Analytical Tools and Models
Throughout his career, Javed Shaik has been instrumental in developing several impactful analytical tools and models. Notable projects include a Servicer Capacity Scenario analyzer app with R-Shiny for FHFA, which forecasts mortgage servicing capacity over a five-year period. He also utilized K-Prototype clustering and PCA to classify mortgage servicers' business models, a variable now used in multiple data pipelines.