Zachary Seeskin
Zach is a senior statistician with NORC at the University of Chicago, where for nearly a decade he has worked on sample design, estimation, and data analysis for government and public interest surveys.
Zach contributes to weighting, total survey error analysis, small area estimation, imputation, and adaptive design for such surveys as the National Immunization Survey, the General Social Survey, the Survey of Doctorate Recipients, and Jewish community studies. Additionally, his expertise includes analyzing administrative data quality and combining data sources for evidence-building, topics on which he has published research in the Statistical Journal of the International Association of Official Statistics and the International Journal of Population Data Science. He has led analysis and reporting for projects centered on data quality assessment and data integration, ranging from ASPE’s Big Data or Not initiative to data quality tool development for the National Center for Science and Engineering Statistics to a National Secure Data Service-Demonstration effort on using AI to enhance data quality and data integration.
Zach is the co-chair of the AAPOR Transparency Initiative and a team member for the American Statistical Association's Assessing the Health of the Federal Statistics Agencies project. Zach also serves on the AAPOR Standards Committee and AAPOR Code Review Committee. He taught in the Public Policy and Administration program of Northwestern University's School of Professional Studies.
Zach earned his PhD in Statistics from Northwestern University in 2016, where he served as a U.S. Census Bureau Dissertation Fellow.
Quick Links
Education
PhD
Northwestern University
MS
Northwestern University
BA
Brandeis University
Appointments & Affiliations
Instructor
Northwestern University, Master’s in Public Policy and Public Administration Program
Committee Member
AAPOR Standards Committee
Co-Chair
AAPOR Transparency Initiative Coordinating Committee
Honors & Awards
Dissertation Fellow | 2016
U.S. Census Bureau
Project Contributions
Publications
-
AI Framework Helps Federal Agencies Improve Data Quality & Integration
NORC Article | January 28, 2026
-
A Framework for Using AI Responsibly with Federal Datasets
Innovation Brief | January 26, 2026
-
"AI-DQSI Framework Plan: Artificial Intelligence for Enhancing Data Quality, Standardization, and Integration"
Project Report | September 1, 2025
-
"Error Profile for the 2023 NIS-Child"
Project Report | September 1, 2024
-
"Error Profile for the 2023 NIS-Teen"
Project Report | August 1, 2024
-
opens in new tab"A Data Quality Scorecard to Assess a Data Source’s Fitness for Use."
Journal Article | April 1, 2024
-
opens in new tab"Estimating County-Level Vaccination Coverage Using Small Area Estimation with the National Immunization Survey-Child."
Journal Article | January 25, 2024
-
opens in new tab“Error Profile for the 2022 NIS-Child.”
Journal Article | November 1, 2023