I am going to do something a little different, and hopefully it feels a bit more honest than the typical AI garbage. I am going to tell you the story of my career, as of writing this (2024).Early in my career, I wanted to be a clinical statistician, working on large-scale longitudinal trials. I started my career at Fred Hutch Cancer Research Institute, but not in the capacity I desired. I was not working as a statistician, but as an informatics programmer, a title that is more closely aligned with data engineering today. While this wasn't the position I wanted, it changed my career forever as it introduced me to the tools and concepts used in data engineering that I would use every day. At the time, Google's AlphaGo beat the best human Go player, and I knew that data science and AI were the future. The next day, I pivoted my career to work in data science.I started at Ookla as a Data Engineer, with the objective to service data science and learn as much as possible about tech. The pace of work was much faster than research, which took some adjusting. I quickly started working out of my scope as a data engineer, working more and more on data science. Roughly 20% data engineering, 80% data science. I work as part of a team, building internal data science tools, designing metrics, analyzing data, and advising on all things data science. I am fortunate to benefit from good mentoring and a community of data scientists and other academia ex-pats that make the work fulfilling and interesting. My work today is primarily focused on automating data analysis and building/maintaining the tools that support those automations. Ookla is my professional home, and while we are going through a lot of growth as a company, I am constantly amazed at the resiliency of my team and the products we build.