I am an early career data scientist with experience managing ecological data in a range of formats. In my personal research I pursued projects that allowed me to blend field, analytical, and modeling approaches to ask and answer community-level questions; this approach gave me first hand experience in both the tidying of raw data and the analysis and visualization of wrangled data.
The bulk of my coding experience is in R but I have also self-taught R Shiny (Shiny app link, code link) and feel confident that I could gain basic competency in other coding languages as needed to accomplish data management tasks. I firmly believe in transparency in data management and I use the version control system Git to make all of my code public upon publication of the manuscript the code supports. My code is well-commented and is meant to provide a clear window into the function and rationale of each operation.
In addition to data management, teaching and communicating scientific results are also things I love and hope to continue to focus on as my career develops. While a graduate student I sought out opportunities to teach and have guest lectured and taught labs in ecology, introductory biology, and multivariate analysis. In April of 2018 I was invited to record an episode of the podcast Science in Progress (created by the Ecological Society of America’s Student Section); give it a listen if you have a few minutes!