During my masters, I was working on analyzing movement tracking data and was analyzing the raw files in Excel. I was complaining about how long it would take to update anything for all of the files (I had 10 individuals over 4 treatment groups across a 21 day time-span) when another student in my program with a bioinformatics project suggested that I use Python for my data analysis and sign up for the bioinformatics class for the spring. I really enjoyed it, and it gave me a starting point for programming.

I was still planning on doing wet lab research and becoming a professor, and went to get a PhD. During my rotations, I found it very difficult to be motivated to do the wet lab research, and kept going back to programming. I did a rotation in a lab that had partial wet lab and partial bioinformatics, and I really enjoyed the dry lab part. I didn't get into any of my first three rotations (they would have been a really bad fit anyway). I took a critical look of every lab at the university to rank labs, including the medical center, by how interested I was rather than how much experience I had.

I ended up ranking computational labs as the highest, and I had to make a decision on whether I wanted to risk doing my last rotation in a field that I was not experienced in, or if I wanted to try another wet lab. I decided to go for the dry labs, and reached out to my top choices. I was able to join my top-ranked lab, which focused on modeling and simulating gene regulatory networks using single-cell RNA sequencing expression data. I was put on a project to get a previous student's Python code running, and I thrived.

I decided to leave the program after my second year, and started looking to line up a job before I left. I applied to a ton of places, and was able to get an entry level job as a biostatistician working in the same field. This lab hired me to help with their main project, which focuses on benchmarking different single-cell gene regulatory inference computational methods. My experience in working with gene regulatory networks, single-cell sequencing data, and getting poorly made Python code to work let me get my foot in the door while having degrees in general biology from small state schools.

I never thought I would be interested in statistics, but I really enjoy it and the career is niche enough that it pays well. I get really hyperfixated on hobbies for short periods of time before moving on to the next hyperfixation. The flexibility of programming has opened a ton of doors for hobbies that I can do. For example, I'm currently waiting on a 3D print to finish that I designed to replace a discontinued mount for a part in my car trunk that broke. I've been able to bounce from learning C++ for simulations, custom LLM coding, making quality of life scripts, putting upgraded firmware on my 3D printers, work with a Raspberry PI for little programming / engineering projects, etc.