Claire Herdeman is a Data & Program Analyst and Computational Analytics and Public Policy master's candidate at the University of Chicago, bringing eight years of experience building data infrastructure and applied models for government, nonprofit, and campaign settings. She has led end-to-end ETL and cloud workflows at Augintel—bringing AWS, Prefect, Terraform, and CI/CD into production for high-volume NLP pipelines—and most recently supported data engineering efforts at the CDC Foundation and the Illinois State Treasurer’s office. Her background spans predictive modeling for criminal justice and mental health interventions, voter outreach modeling on a national presidential campaign, and automating message-testing for advocacy groups, reflecting a blend of technical rigor and policy-driven impact. An active QA/test automation contributor to the city-scrapers project, she focuses on reliable data ingestion from messy public sources, a skill that complements her policy-oriented analysis. Comfortable translating between technical and non-technical stakeholders, she repeatedly leads client integrations and cross-functional meetings to turn complex domain logic into auditable pipelines.
8 years of coding experience
6 years of employment as a software developer
Master of Science - MS Computational Analytics and Public Policy, Master of Science - MS Computational Analytics and Public Policy at Harris School of Public Policy at the University of Chicago
B.S. Economics and B.A. German Studies, B.S. Economics and B.A. German Studies at Carnegie Mellon University
Scrape, standardize and share public meetings from local government websites
Role in this project:
QA Engineer / Test Automation Engineer
Contributions:6 reviews, 52 commits, 8 PRs in 2 years 9 months
Contributions summary:Claire's commits primarily involve writing and updating tests for the `chi_localschoolcouncil` spider within the `city-scrapers` repository. Their work includes modifying test files, adjusting assertions to reflect updated data, and ensuring the correct parsing of meeting data. The user refactored existing test structures by copying and updating from other existing tests to the `chi_localschoolcouncil` test file.
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