Matthew Lapa
About Matthew Lapa
Matthew Lapa is a Decision Scientist at US Foods, where he has worked since 2023. He holds a PhD in Physics from the University of Illinois Urbana-Champaign and has transitioned from a career in theoretical physics to data science.
Work at US Foods
Matthew Lapa has been employed as a Decision Scientist at US Foods since 2023. In this role, he utilizes data-driven methodologies to support decision-making processes within the organization. His work is conducted remotely, allowing him to contribute to the company's objectives from a flexible location. The position leverages his expertise in data science to analyze complex datasets and provide actionable insights.
Education and Expertise
Matthew Lapa earned his Doctor of Philosophy (PhD) in Physics from the University of Illinois Urbana-Champaign, where he studied from 2012 to 2018. Prior to this, he completed a Bachelor of Science (BS) in Engineering Physics at Cornell University from 2008 to 2012. His academic background provides a strong foundation in theoretical and mathematical concepts, which he has successfully transitioned into the field of data science.
Background
Before joining US Foods, Matthew Lapa worked as a Data Scientist at Three Five Two from 2021 to 2023. His previous experience includes a three-year tenure as a Postdoctoral Researcher at the University of Chicago, where he further developed his research skills. This diverse background in academia and industry has equipped him with a unique perspective on data analysis and its applications.
Achievements
Matthew Lapa has published academic papers available on arXiv, showcasing his research contributions in the field of physics. He has also initiated a blog where he discusses technical topics related to statistics and data science, reflecting his commitment to sharing knowledge and insights. Additionally, his GitHub profile features examples of his work, highlighting his practical applications of data science techniques.
Career Transition to Data Science
Matthew Lapa transitioned from a career as a theoretical and mathematical physicist to data science. This shift allowed him to apply his analytical skills in a new context, focusing on data-driven solutions. His background in physics provides him with a unique approach to problem-solving within the data science domain.