My name is Dan. I am a developer advocate at Databricks. I focus on MLops and LLMops.
Before starting at Databricks, I worked at bit.io, which offered the fastest way to get a cloud PostgreSQL database. In my role, I used bit.io databases to manage the data in various data science research projects. I've analyzed data and written about topics such as methane emissions, Bayesian statistics, labor turnover, the size of the House of Representatives, and much more.
Prior to starting at bit.io, I was the Director of Data Analytics at the Guinn Center for Policy Priorities in Las Vegas, Nevada. In this role, I was responsible for conducting data-driven policy analysis and for supporting ongoing research and data analysis at the Guinn Center. Most of my work a the Guinn Center focused on public health, climate change, and the 2020 Census.
I received a B.A. in the History of Science at the University of Chicago in 2013. Following this, I worked for several years for Context Matters, Inc., a data analysis company focused on global pharmaceutical reimbursement policies and data. While working at Context Matters, I wrote extensively about drug pricing and reimbursement practices. I went on to receive an M.S. in Statistics from the University of Minnesota, Twin Cities. My thesis work comprised a simulation-based comparison of statistical tests for heterogeneity of risk difference between patient subgroups in clinical trials.
In 2018, I was a graduate research fellow in the Data Science for the Public Good program at the Biocomplexity Institute of Virginia Tech. I have also worked on a variety of projects for for-profit and nonprofit organizations as a statistics and data science consultant.