Bijal Shah
About Bijal Shah
Bijal Shah is a Senior Manager in Analytics and Modeling at Fannie Mae, with expertise in AI, Machine Learning, and Data Science.
Company
Bijal Shah currently serves as a Senior Manager in Analytics and Modeling at Fannie Mae. Based in the Washington DC-Baltimore Area, Bijal leads a team dedicated to risk analytics, reporting, and modeling. Fannie Mae is a leader in providing financial products and services, where Bijal's role is crucial for supporting evidence-based decision-making within the organization.
Title
Bijal Shah holds the position of Senior Manager, Analytics and Modeling at Fannie Mae. In this capacity, Bijal directs a team that focuses on the development of risk analytics and models. This work supports the company’s goal of enhancing business intelligence through advanced reporting and dashboard creation.
Education and Expertise
Bijal Shah earned a Master of Science in Electrical & Computer Engineering from the New York Institute of Technology between 2003 and 2005. Additionally, Bijal holds a Bachelor of Science in Instrumentation & Control from Government Engineering College - Gandhinagar, Gujarat, completed between 1999 and 2002. Bijal's technical expertise includes Artificial Intelligence, Machine Learning, Data Science, and proficiency with tools such as Tableau, MicroStrategy, R, and Python.
Professional Background
Bijal Shah has developed a strong background in risk analytics and modeling, with a particular emphasis on creating metrics, reports, and dashboards for business intelligence purposes. Bijal's role at Fannie Mae involves aiding in evidence-based decision-making by leveraging advanced analytics and developing models to assess and mitigate risks.
Achievements
One notable achievement by Bijal Shah is the filing of a patent entitled 'Systems and Methods for Visualization based on Historical Network Traffic and Future Projections of Infrastructure Assets' on July 23, 2019. This patent exemplifies Bijal's innovative contributions to the field of analytics and modeling, particularly in the context of network traffic and infrastructure asset projection.