Marco Ragone
About Marco Ragone
Marco Ragone is a Senior Data Scientist at CCC Intelligent Solutions in Chicago, Illinois, where he has worked since 2022. He holds a PhD in Machine Learning and Deep Learning from the University of Illinois Chicago and has extensive experience in data science and engineering.
Work at CCC Intelligent Solutions
Marco Ragone has been employed at CCC Intelligent Solutions since 2022, serving as a Senior Data Scientist. His role involves leveraging data science techniques to enhance operational efficiency and drive insights within the organization. Prior to this position, he worked as a Data Scientist at the same company from 2021 to 2022. Both roles were based in Chicago, Illinois, where he contributed to various projects aimed at improving data-driven decision-making.
Education and Expertise
Marco Ragone holds a Doctor of Philosophy (PhD) in Machine Learning, Deep Learning, and Materials Science from the University of Illinois Chicago, where he studied from 2018 to 2022. He also earned a Master's degree in Energy Engineering from Politecnico di Torino, completing his studies from 2016 to 2018. Additionally, he obtained a Master's degree in Mechanical Engineering from the University of Illinois Chicago in 2018. His educational background equips him with a strong foundation in data science and engineering principles.
Background
Marco Ragone began his academic journey at Politecnico di Torino, where he earned a Bachelor's degree in Energy Engineering from 2012 to 2016. He later transitioned to the University of Illinois Chicago, where he pursued advanced studies in Mechanical Engineering and subsequently in Machine Learning and Deep Learning. His experience as a Research Assistant at the University of Illinois at Chicago from 2018 to 2022 further solidified his expertise in data science and research methodologies.
Previous Experience
Before his current role, Marco Ragone worked as a Research Assistant at the University of Illinois at Chicago from 2018 to 2022. In this position, he engaged in various research projects that contributed to his knowledge and skills in data analysis and machine learning. His transition from academia to industry reflects his ability to apply theoretical knowledge to practical applications in data science.