Data Science
I’m a quantitative social scientist specializing in causal inference, data & project management, and analysis. Through my PhD research and an NSF-funded data project, I have designed randomized survey experiments, built original datasets from scratch, and carried analyses from raw data to peer-reviewed publication, both with coauthors and independently. I’m interested in data science and analyst roles on experimentation, causal inference, and policy research teams.
Portfolio
Background
Causal inference and experiments. Randomized survey experiments, instrumental variables, power analysis, and weighted, clustered estimation.
Building data from scratch. As data and project manager for the NSF-funded Charters of Intergovernmental Organizations project, I managed a team of research assistants building a dataset of roughly 20,000 observations, and I cleaned and managed the data in R.
Text as data. A Malyi Center–funded project tracing the origins of election denialism through town hall transcripts and politicians’ speeches.
Communicating results. Peer-reviewed publications, including in Democratization, and two years as a full-time teaching assistant in political science at the University of Chicago, including research methods.
Skills & Education
Languages: R (advanced, six years), Python (intermediate), SQL (beginner)
Tools: tidyverse, estimatr, ggplot2, pandas, statsmodels, linearmodels, Quarto and R Markdown, Git and GitHub
Methods: randomized experiments, instrumental variables, regression, power analysis, survey weighting, text analysis
Education:
- M.S. in Analytics, Georgia Institute of Technology (in progress)
- PhD program in Political Science, University of Chicago (coursework complete)
- M.A. in Social Sciences, University of Chicago, 2023
- B.A. in Political Science and B.A. in International Affairs, East Tennessee State University, 2022