Jared Grooms

I am a recently graduated Masters in Statistics from Brigham Young University with a Bachelor's degree in Biostatistics. My research and work experience span various fields, demonstrating my passion for statistics and its applications.

Most recently, I held the position of Research Assistant in the Statistics Department at BYU. In this role, I was actively involved with modeling the length of professional tennis rallies and estimating parameter differences between court surfaces, serve type, players, and set versus tiebreaker differences. 

In addition to my current role, I have gained valuable experience working with the Record Linking Lab, where I used machine learning models to link World War 1 death data to US census records. Alongside this project I led the Database of Human Lifespan Team where we scraped genealogical data to build the largest death database in the world. Using this data in conjunction with historical US Census data, I performed research on US first born premiums between 1850 - 1940 and published the findings in my latest paper. 

I also had the opportunity to work for Black Hills Energy with Christopher Gwerder over the summer of 2024. As the BHE Data Science intern I was tasked to use traditional statistical models to asses significant weather variables on energy usage as well as fit various XGBoost and times series models. These models were then put into production to forecast gas usage across each of the company's business zones which spanned from South Dakota to Arkansas. 

Currently I am building the first crowd sourced, publicly available wrestling dataset that charts the actions and reactions of all scoring attempts for collegiate wrestling matches. I hope to facilitate an easy and straightforward methodology for wrestling data collection that can still provide valuable insights to professional and non professional audiences. This project is known as Wrestle Collect and interested volunteers or data consumers can reach out to me personally to learn how to contribute or access the dataset themselves for free.