Tom Schimoler
About Tom Schimoler
Tom Schimoler serves as the Principal Machine Learning Scientist Lead at Apex Clearing Corporation, where he has worked since 2021. He has extensive experience in data science and machine learning, having held positions at notable companies such as Expedia Group and Orbitz Worldwide, and he has contributed to academia as an instructor and research fellow at DePaul University.
Work at Apex Clearing Corporation
Tom Schimoler has served as the Principal Machine Learning Scientist Lead at Apex Clearing Corporation since 2021. Based in Chicago, Illinois, he focuses on developing machine learning solutions that address user-facing challenges. His role involves leveraging advanced data science techniques to enhance the company's offerings in the fintech sector.
Previous Experience in Data Science
Before joining Apex Clearing Corporation, Tom Schimoler worked at Expedia Group as a Senior Data Scientist from 2016 to 2021. In this position, he concentrated on creating machine learning solutions for various applications, including hotel recommendations and revenue management. Prior to Expedia, he held the role of Machine Learning Engineer II at Orbitz Worldwide for one year and worked as a Data Science Consultant for three years.
Academic Background and Teaching Experience
Tom Schimoler earned his Master of Science in Computer Science from DePaul University, where he studied from 2005 to 2007. He also holds a Bachelor of Science in Mathematics from Dominican University, completed between 1995 and 1998. At DePaul University, he served as an Instructor from 2009 to 2013, teaching subjects such as statistics, algorithms, and Java programming. Additionally, he worked as a Research Fellow at the same institution for six years.
Research Contributions and Publications
Throughout his career, Tom Schimoler has co-authored over a dozen papers presented at leading conferences in the fields of recommender systems and information retrieval, including Recsys and CIKM. His research has contributed to advancements in machine learning applications, particularly in user-facing technologies.