Kevin Powell
About Kevin Powell
Kevin Powell is a Data Scientist with a strong background in chemistry and applied mathematics. He has over a decade of experience in tutoring and currently works at Redhorse Corporation, where he specializes in explainable artificial intelligence and data visualization.
Work at Redhorse
Kevin Powell has been employed at Redhorse Corporation since 2019, serving as a Data Scientist. His role involves analyzing complex data sets and applying advanced data science techniques. Prior to his current position, he completed a two-month internship at Redhorse Corporation in 2019, where he gained practical experience in data science. His work at Redhorse is based in Arlington, VA.
Education and Expertise
Kevin Powell holds a Bachelor of Science (B.S.) degree in Chemistry from Virginia Commonwealth University, where he studied from 2003 to 2009. He furthered his education by obtaining a Master of Science (M.S.) in Applied Mathematics from Indiana University of Pennsylvania between 2017 and 2019. Additionally, he earned an Associate of Science (A.S.) in Medical Laboratory Technology from Community College of Allegheny County from 2011 to 2013. His educational background supports his expertise in data science and mathematics.
Background
Before joining Redhorse Corporation, Kevin Powell worked as a Medical Technologist at St. Clair Hospital from 2013 to 2018. This role provided him with valuable experience in the medical field, contributing to his understanding of data in healthcare contexts. He has also accumulated over a decade of experience as a math and science tutor, which has enhanced his ability to communicate complex ideas effectively.
Achievements in Data Science
Kevin Powell specializes in explainable artificial intelligence, focusing on making AI models more transparent and understandable. He is skilled in creating compelling explanatory visuals that aid in the comprehension of complex data. His expertise includes dimension reduction and classification techniques, which are essential for simplifying data and improving model performance. Additionally, he enjoys working on network analysis, examining relationships and interactions within data sets.