Scott Davis
About Scott Davis
Scott Davis is a Senior Data Scientist at MVP, where he has worked since 2022. He has a strong academic background in Physics, holding a PhD from Florida State University and has contributed to various research projects, including work with the James Webb Space Telescope.
Work at MVP
Scott Davis has been employed at MVP since 2022, currently holding the position of Senior Data Scientist. In this role, he has contributed to various projects, including the development of automation codes for professional services and the prototyping of new processes utilizing technologies such as Spark, SQL, and Python. Prior to his current position, he worked as a Data Scientist at MVP from 2021 to 2022, where he created a random forest model that achieved 80% accuracy in determining brand sponsors using social media data.
Education and Expertise
Scott Davis holds a Doctor of Philosophy (PhD) in Physics from Florida State University, which he completed between 2017 and 2020. He also earned a Master of Science (MS) in Physics from the University of Florida from 2015 to 2017. His undergraduate studies culminated in a Bachelor of Science (BS) in Physics from Clemson University, completed in 2015. His academic background provides a strong foundation for his expertise in data science and analytics.
Background
Before joining MVP, Scott Davis gained valuable experience in academia and research. He served as a Graduate Research and Teaching Assistant at Florida State University from 2015 to 2020. He also worked as a Research Assistant at Clemson University from 2013 to 2015. Following his doctoral studies, he was a Postdoctoral Researcher at the University of California, Davis for 10 months in 2020 to 2021, where he further developed his research skills.
Achievements
Scott Davis has contributed to significant research initiatives, including serving as Co-Principal Investigator for successful proposals related to the James Webb Space Telescope cycle 1. He has also implemented advanced data analysis techniques, such as disaggregating viewership data to create marketing personas based on Formula 1 and NASCAR race viewership habits. Additionally, he utilized natural language processing (NLP) to identify similar social media posts across platforms, which resulted in a 60% reduction in quality assurance time.