Summary
Nora Kirkizh is a quantitative researcher and soon-to-be PhD in Computational Social Science who combines over a decade of research experience with hands-on expertise in survey methodology, causal inference, and computational methods. Based in New York, she has led large-scale survey experiments, analyzed millions of social media URLs, and translated messy observational data into publishable, policy-relevant findings for think tanks and research labs. Fluent in R and Python, she bridges rigorous statistics, feature engineering, and machine learning to build reproducible pipelines and compelling visualizations. A Facebook Fellowship 2020 finalist, Nora has a track record of turning constrained natural experiments into benchmark studies that secured further funding and journal publications. She is equally comfortable presenting insights to academic audiences and shaping product-facing research for consulting clients.
10 years of coding experience
7 years of employment as a software developer
Master of Arts - MA, Sociology, Computational Social Science, Master of Arts - MA, Sociology, Computational Social Science at Higher School of Economics
Doctor of Philosophy - PhD, Political Science, Computational Social Science, Doctor of Philosophy - PhD, Political Science, Computational Social Science at Technical University of Munich
Diploma, Journalism, Diploma, Journalism at Saint Petersburg State University
Master's degree, Political Science, Quantitative Methods, Master's degree, Political Science, Quantitative Methods at University of Mannheim
Research visit and cources, Economics of Media, Causal Inference, Research visit and cources, Economics of Media, Causal Inference at Universitat Pompeu Fabra