Connor Richards
About Connor Richards
Connor Richards is a Machine Learning Engineer with extensive experience in data science and research. He has worked at prestigious institutions such as CERN and Princeton University, and currently holds positions at SpineZone and Earthshot Labs.
Current Role at SpineZone
Connor Richards serves as a Machine Learning Engineer at SpineZone, a position he has held since 2021. In this role, he focuses on developing and implementing machine learning models to enhance the company's data-driven solutions. His work contributes to SpineZone's mission of improving patient outcomes through innovative technology.
Previous Experience at CERN
Connor Richards worked at CERN in Geneva, Switzerland, in two capacities. He was an Undergraduate Researcher from 2013 to 2016, where he gained foundational experience in research methodologies. Following this, he served as a Graduate Student Researcher from 2016 to 2018, furthering his expertise in high-energy physics and data analysis.
Experience at Civis Analytics
At Civis Analytics, Connor held two positions. He worked as an Applied Data Engineer for three months in 2020, where he contributed to data engineering projects. Subsequently, he served as an Applied Data Scientist from 2018 to 2020 for two years, focusing on data analysis and modeling to support various client initiatives.
Educational Background
Connor Richards earned a Bachelor of Science in Physics from the University of California, Riverside, completing his studies from 2013 to 2016. He then pursued a Master’s Degree in Applied Mathematics and Theoretical Physics at the University of Cambridge, studying from 2016 to 2017. His education provided a strong foundation in both theoretical and applied aspects of physics and mathematics.
Research at Princeton University
Connor was a Physics PhD Student at Princeton University for eight months in 2017 to 2018. During this time, he engaged in advanced research in physics, contributing to academic projects and gaining experience in a rigorous academic environment.