I’m a data scientist interested in experimentation, hypothesis testing, causal inference, and the nuances of data about people. I’m the Tech Lead for Online A/B Experimentation at Pinterest, where I work to improve the experiment design quality across engineering and make our A/B testing platform simpler run high quality experiments.
As a statistician, I believe that we should always question assumptions and look at the big picture, not just the p-value. I enjoy uncovering opportunities to make other peoples’ work easier and more trustworthy, and designing tools to bridge that gap.
I received my PhD in Statistics from UC Berkeley in 2019. I was a Fellow at the Berkeley Institute for Data Science and my advisor was Philip Stark. My PhD work lies at the intersection of Statistics and Social Good. I’ve analyzed data to uncover gender bias in teaching evaluations, evaluate the impact of nutritional policies, and uncover anomalous results in U.S. elections.
Outside of statistics, I am passionate about food and fitness.
LinkedIn: https://www.linkedin.com/in/kellieottoboni/ Twitter: @kellieotto