Joseph Davis

Data Scientist @ American Electric Power

About Joseph Davis

Joseph Davis is a Data Scientist with a background in Economics and Statistics. He has experience in various roles, including data analysis and application development, and currently works at American Electric Power.

Work at American Electric Power

Joseph Davis has been employed at American Electric Power since 2020, where he holds the position of Data Scientist. His role involves applying advanced data science techniques to enhance operational efficiency and decision-making processes. Prior to his current position, he served as a Data Science Intern at the same company for two months in 2019. During his internship, he gained practical experience that contributed to his development in the field.

Education and Expertise

Joseph Davis earned a Bachelor of Science in Economics from The Ohio State University, completing his studies from 2016 to 2018. He furthered his education by obtaining a Master of Science in Statistics from Miami University, where he studied from 2018 to 2020. His educational background provides a strong foundation in both economic theory and statistical analysis, equipping him with the skills necessary for data-driven decision-making.

Background

Joseph Davis began his career as a Student Administrative Assistant at The Ohio State University Department of Athletics from 2017 to 2018. He later worked as a Graduate Assistant at Miami University for nine months in 2018-2019. His early experiences in these roles helped him develop organizational and analytical skills that he applies in his current data science work.

Achievements in Data Science

In his role as a Data Scientist at American Electric Power, Joseph Davis developed an internal shiny application designed for microarray experiments analysis. This application performs quality control checks and generates graphics and results, which are exported into an Excel file. Additionally, during his time at Procter & Gamble as a Data & Modeling Sciences Contractor, he utilized Bayesian methods for compositional analysis on microbiome data, creating interactive visualizations to represent complex datasets.

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