Igor Geyn, PhD
Applied Data Scientist
Causal inference · Econometrics · Machine learning
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 defended my dissertation and received my PhD from UCLA in February 2026.
I am currently on the (non-academic) job market exploring ways to make an impact using my skills as a researcher, analyst, and experienced business professional. If you are interested in connecting, I am available via LinkedIn, email, and Twitter/X.
Current Work
I'm helping several companies and organizations with causal inference and statistical analysis, including as a part-time economist with The Burning Glass Institute, where my work helps answer questions about the future of work, the impact of AI on demand for skills, and various regional workforce programs.
Job Market Interests
I'm broadly interested in quant research and data science opportunities on the product and policy/regulatory sides of for-profit/industry organizations, including in technology but also other domains. (I'm interpreting "product" very broadly: this can include services, consulting, internal strategy, etc. as outputs.) I've previously worked in government contracting/consulting, marketing and market research, the energy and utility space, ride share/micromobility, and health.
Some job titles I've used in the past: data scientist, senior data scientist, researcher, applied researcher, data analyst, senior data analyst, and applied data scientist.
Feel free to explore my LinkedIn profile to learn more.
Some More About Me
My dissertation uses real-world, non-lab data and causal research methods (e.g., regression discontinuity design and diff in diff) 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 have experience teaching statistics and data science/research methods, both formally (through 3+ years of teaching at UCLA) and as a tutor/coach for college students and young working professionals.
I've been lucky enough to develop my data skillset alongside growth in the sophistication of data analysis overall, having 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 achievements follow.
- Managing a team of researchers to execute the flagship research product, Leading Brands in Government, 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.