Igor Geyn

Hello! My name is Igor. I live and work in the San Francisco Bay Area.

I'm a data scientist and quantitative researcher specializing in causal inference using large data sets, advanced econometric methods, complicated data pipelines, and machine learning.

I am currently on the non-academic job market as I finish up my PhD at UCLA (expected Jan-Feb 2026). My dissertation uses real-world, non-lab data and causal research methods (regression discontinuity design, diff in diff, and other methods) to provide concrete answers to long-standing questions:

• Does attending college make people more active citizens? (Yes, by quite a bit.)
• Does direct democracy create a more informed electorate? (It depends on how you measure it, but probably not.)
• Is the administration of U.S. elections fair or biased in a partisan way? (Fair, but it matters who is in office.)

I also teach statistics and data science/research methods to high school students, college students, and working professionals.

I've been lucky enough to develop my data skillset alongside growth in the industry overall; I've worked in every type of infrastructure/computing environment from scattered Excel workbooks to robust, integrated cloud pipelines. This has required me to learn a lot more than just coding and analysis: stakeholder management, task scoping, people management, and generally how to make do with what you have while working/building towards a better future state. Some examples of my project-level achivements follow.

• Managing a team of researchers to execute the flagship research product at the public sector division of Atlantic Media.
• Piloting and executing the first explicitly causal analysis of wildfire prevention programs at Pacific Gas & Electric Co. (PG&E).
• Launching the first political science program at UCLA to teach undergraduates research skills by pairing them with high-quality, in-flight graduate research projects and teaching modern-day research methods.