Dima M.
About Dima M.
Dima M. is the Lead Data Scientist for Enterprise Analytics and Modeling at Fannie Mae, with extensive experience in data science, risk management, and financial analysis.
Title
Dima M. serves as the Lead Data Scientist for Enterprise Analytics and Modeling at Fannie Mae.
Current Role at Fannie Mae
As the Lead Data Scientist, Dima M. is responsible for leading the implementation of new statistical modeling capabilities at Fannie Mae. This role requires coordinating various processes, systems, and stakeholders to support risk measurement, financial valuation, decision-making, and business performance. Dima applies advanced predictive analytics to enhance business applications and develops data visualizations, technical documentation, and non-technical presentation materials to effectively communicate solutions to business partners.
Previous Experience
Dima M. has an extensive background in data science and financial analytics. From 2019 to 2022, Dima worked at Tautology as a Data Science Specialist focusing on Explainable Machine Learning. Prior to this, Dima spent a decade at the World Bank Group as an Enterprise Risk Manager specializing in Data Analytics and Reporting. Earlier roles include a position at FOCUS Investment Banking as a Financial Associate in Private Equity Valuation from 2007 to 2009 and a role at Citi as an Investment Analyst specializing in Principal Protected Notes from 2002 to 2007.
Education and Expertise
Dima M. has earned multiple degrees from reputable institutions. Dima completed a Master of Science in Information Technology at Virginia Tech in 2019, a Master of Science in Finance from The George Washington University in 2017, and a Bachelor of Arts in Economics from the University of Colorado Denver between 2010 and 2012. This diverse educational background supports Dima's expertise in data analytics, financial valuation, risk management, and predictive modeling.
Professional Contributions at Fannie Mae
In the role of Lead Data Scientist, Dima M. has significantly contributed to Fannie Mae's operational efficiencies by designing new modeling applications. These applications support risk measurement, financial valuation, decision-making, and business performance. Dima has also developed technical documentation and non-technical materials to present high-impact solutions, enhancing the organization's predictive analytic capabilities and overall business application delivery.