Paul Melby
About Paul Melby
Paul Melby is the Vice President of Data Science at Enigma Technologies, Inc., where he leads efforts in data analysis and machine learning. He has extensive experience in various domains, including fraud detection and aviation, and has held senior roles at organizations such as Vesta, Capital One, and MITRE.
Work at Enigma
Paul Melby currently holds the position of Vice President, Data Science at Enigma Technologies, Inc., a role he has occupied since 2023. In addition to this position, he serves as the Head of Entity Resolution Data Science, a role he has held since 2022. His work involves providing technical coaching to data science teams and leveraging his extensive experience to address complex data challenges.
Education and Expertise
Paul Melby earned a Bachelor of Science in Physics from Ohio University, studying from 1993 to 1997. He furthered his education at the University of Illinois Urbana-Champaign, where he obtained both a Master of Science and a Ph.D. in Physics between 1997 and 2002. Additionally, he was a National Science Foundation Fellow at the Santa Fe Institute from 2000 to 2001. His expertise includes solving data analysis problems in various domains, such as fraud detection, intelligence, and aviation.
Background
Before joining Enigma Technologies, Paul Melby held several significant positions in the field of data science and engineering. He worked as Senior Vice President of Artificial Intelligence and R&D at Vesta from 2021 to 2022. Prior to that, he served as Senior Director of Data Science at Capital One from 2017 to 2020 and as a Lead Data Mining Engineer at MITRE from 2006 to 2012. He also gained experience as a Postdoctoral Researcher at Georgetown University from 2002 to 2005 and as a Forward Deployed Engineer at Palantir Technologies from 2012 to 2014.
Achievements
Throughout his career, Paul Melby has guided teams in developing machine learning models that generate significant annual value, amounting to hundreds of millions. He emphasizes the importance of collaboration among data scientists, engineers, product teams, and domain experts to effectively tackle real-world problems. His work incorporates a variety of methods, including text mining, classification, graph analysis, and anomaly detection.