I got a PhD in Quantitative Psychology. I learned about latent variable modeling, applied statistics and some machine learning. My dissertation used Bayesian and conditional hierarchical statistical models to analyze dual-system models of decision making. Some of this work, especially a paper re-interpreting the results of a very popular cognitive test turned out to be popular in the field. But I realized that the parts of the job I really liked involved applying the methods I was learning about to new problems rather than developing new ones, and I enjoyed coding. So after considering taking several postdocs none of which felt right for my career, I went to a data science bootcamp that helped fill out my machine learning skills, learn about cloud computing, and big data tools like Spark and Hadoop. I used what I learned to make a small app.

Then, I got a job at Via. Via was and still is (at the time of this writing) a quickly growing and evolving company. When I joined, many key aspects of the business, like pay, were still handled in excel sheets. After a few smaller projects in demand prediction and fraud detection, I was fortunate enough to help develop the algorithms underlying the supply side of the business. This involved quite granular prediction of driver behavior and optimization of incentive allocation. Because this project was close to the core of what Via does, I worked with most sides of the business: software and data engineering, data science, operations and finance. I learned about how growing tech companies work and how optimization can be used to put machine learning predictions to work. I gave a talk at a meetup about one of my major projects.

At the time of this writing, I am starting a job in quantitative user experience research at Google. I am hoping to learn about how larger and more developed companies function and try my hand at user experience at a company that perhaps knows it best.