Soubhik is a research methodologist in the Methodology and Quantitative Social Sciences department at NORC at the University of Chicago. He specializes in the application of statistical and computational methods to the social sciences. Soubhik has over seven years of experience applying a diversity of data science tools including machine learning, generative AI, causal inference, natural language processing, and data visualization to substantive applications ranging from social media text analysis to government program evaluation to pre-election polling.
At NORC, Soubhik leads a wide range of quantitative social science tasks and projects. On complex survey weighting, he designed, built, tested, and applied a suite of statistical methods in R to combine probability and non-probability surveys for the newly developed Rapid Survey System (RSS) from the National Center for Health Statistics (NCHS). Soubhik also created a reproducible pipeline for weighting Pew Research Center’s Religious Landscape Study III (RLS III), a mixed-mode study involving both dual-frame random digital dialing and address-based sampling. On data visualization, he built a dynamic data visualization dashboard to enable data collection and quality control for the 2023 round of the Survey of Doctorate Recipients (SDR). Additionally, Soubhik is leading the development of a dashboard to diagnose weights for AP-NORC political polls. On causal inference and statistical modeling, Barari led an evaluation of mode effects in the SDR and led an evaluation of generative AI tools for survey interviews. Finally, Soubhik is a project manager and lead author for NORC's assessment on college ranking systems, drawing on best practices from construct validity and measurement theory.
Before joining NORC, Soubhik was a research scientist at SurveyMonkey leading data science efforts on the polling team. He has also served in data science roles at Microsoft Research, Harvard’s Institute for Quantitative Social Science, and MIT’s Political Methodology Lab. Soubhik’s research has appeared in academic journals such as Nature Scientific Data and media outlets such as The Atlantic and Scientific American. Soubhik regularly gives talks on data science, social science, and public opinion at organizations such as the NY Open Statistical Programming Meetup, the R Gov Conference, Columbia University, Meta, AAPOR, and TEDx.
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Education
PhD
Harvard University
AM
Harvard University
BS
Tufts University
Appointments & Affiliations
Programming Chair
NYAAPOR
Adjunct Assistant Professor
Columbia University