John Shriver
About John Shriver
John Shriver is the Director of Data Science at Regrow Ag, with a diverse background in data science, spatial statistics, and GIS. He has held various roles in prominent companies, including Descartes Labs and Premise Data.
Current Role at Regrow Ag
John Shriver currently holds the title of Director of Data Science at Regrow Ag. His role involves leading data science initiatives aimed at leveraging spatial statistics and GIS technologies to address agricultural challenges. He integrates machine learning algorithms into practical solutions, enhancing the company's ability to manage and analyze agricultural data effectively. His background in engineering supports his role in designing data-driven strategies.
Previous Positions
John has a robust professional background, having worked at various companies in data science roles. He served as Director of Platform and Team Lead - Platform Consulting at Descartes Labs, enhancing their platform capabilities. His experience includes time as a Sr. Data Scientist at Premise Data and Planetary Resources, Inc., where he contributed to numerous data-driven projects. Additionally, he was the Lead Data Scientist at FarmLogs and a Statistician at the University of Michigan. His early career includes roles as a Remote Sensing Intern, Program Director at Princeton AlumniCorps, and Petty Officer Second Class in the U.S. Coast Guard.
Educational Background
John holds a Master of Science (M.S.) in Environmental Informatics from the University of Michigan, where he developed strong skills in spatial statistics and GIS. He also studied Islamic Studies at the University of Washington, earning his degree in 2006. Before that, he achieved an Associate of Arts (A.A.) in Physics from Centralia Community College. This diverse educational background enables him to approach problems with a unique interdisciplinary perspective.
Expertise and Skills
John Shriver possesses expertise in spatial statistics and GIS, essential for analyzing geographical data and environmental patterns. He effectively utilizes machine learning algorithms to solve complex business problems. His engineering skills facilitate the design and implementation of data-driven solutions. This combination of expertise makes him a valuable asset in roles that require advanced data science proficiency and practical technology integration.