Kent Orr
19 Avon Pl
Athens, OH 45701

email: orr.kent@gmail.com
website: www.pawpawanalytics.com
github: github.com/kent-orr
stackoverflow: stackoverflow.com/users/12671149/kent-orr

# --------------------------------- Education ----------------------------------

Ohio University
- Masters of Applied Science 
- Recreation and Sport Pedagogy

Ohio University
- Bachelors of Applied Science
- Recreation and Sport Pedagogy

Hocking College
- Associates of Applied Science
- Ecotourism and Adventure Travel

# -------------------------------- Work History --------------------------------

Rocky Brands Inc
- 2023-Present
- Senior Data Analyst

Ohio University
- 2022
- Research Analyst III

Lehigh CustomFit
- 2019-2022
- Digital Marketing Analyst

Rising Appalachian Warriors
- 2017-2019
- Executive Director

# ----------------------------------- Skills -----------------------------------

Programming Languages
- R
  - Shiny framework
  - Plumber APIs
  - ggplot2 / plotly
  - Package Authoring
  - Modelling
  - Preference for data.table / base R
  - Tidyverse
- Julia
  - wrote digital marketing back-end data processing
- Python

- Linux
  - preffered OS
  - use as primary desktop

Familiar Technologies
- Docker
- DigitalOcean
- AWS
- Netlify
- Hugo

# ---------------------------------- About Me ----------------------------------

Experience combining behavioral statistics with programming to develop insghts,
automations, and other actionable intelligence products. I have written a custom
digital marketing integration, deployed docker swarm servers for data 
visualization, API event listening, and data dissemination. Proficient in 
writing proprietary packages to lower the threshold for other team mebers to 
retrieve, upload, clean, or visualize data such as implementing custom graphing 
palletes, simplified connections and uploading with s3 and redshift, or making 
API endpoints available to excel users. R is my preferred and most comfortable 
language followed by Julia. My abiliity to communicate the development and use
of data products to non-technical users has been a significant advantage in my 
career. I find the step between the finished data product, and adoption of 
insights by the humans that act on or implement the outputs of those insights to
be the most crucial but often overlooked step in making data science effective.