I graduated with my PhD in Epidemiology from UCLA in May 2019. During my PhD, I studied the language and statistics of causation, examining whether A caused B and with what strength. My dissertation explored a sub-field of causal inference known as quantitative bias analysis. I found this area particularly attractive because it allows for greater transparency in Epidemiological studies. Instead of simply positing that one or more biases may be impacting one’s results, it allows researchers to quantify these assumptions. Those evaluating a study are ultimately able to use this information in conjunction with a p-value or confidence interval to understand the robustness of a causal estimate.

Since graduating I’ve worked as a scientist at a few different companies including Verana Health, Valo Health, and ClosedLoop. These experiences have given me insight into the development of a clinical data product, a pharmaceutical asset, and an analytics platform. I’ve led a team and managed a junior data scientist. I’ve worked solo to pioneer novel solutions to ambiguously defined problems. I’ve been responsible for client-facing deliverables. Throughout the journey I’ve developed coding expertise across R, python, pyspark, and SQL.

Throughout all these experiences, I’ve maintained a passion for baseball analytics. During graduate school I always chose baseball as the subject for biostatistics class projects. I’ve also contributed a bit to Pitcher List. I will continually return to baseball data as a fun outlet to explore and learn new data science concepts.