Star Ying is a data science leader with 10 years of experience building predictive models, GIS systems, and large-scale analyses across federal agencies and KPMG's federal portfolio. She has driven projects that influence billions in logistics decisions, modernized healthcare analytics production, and designed an address-level GIS for millions of records, blending statistical rigor from a CMU M.S. in Statistics with practical federal program delivery. Her background at the Census Bureau and Department of Commerce informs a strong applied-statistics foundation—kriging, Bayesian search methods, and large-scale data usability—paired with experience managing multi-agency portfolios. Star contributes to open source geocoding infrastructure, adding a US Census Geocoder provider and tests, showing she still enjoys hands-on engineering alongside leadership. Based in Washington, D.C., she thrives on "inelastic" problems that demand creative, auditable solutions bridging data science, policy, and production systems.
10 years of coding experience
9 years of employment as a software developer
Bachelor of Arts (B.A.) Mathematics Economics, Bachelor of Arts (B.A.) Mathematics Economics at Canisius University
Master of Science (M.S.) Statistics, Master of Science (M.S.) Statistics at Carnegie Mellon University
Contributions summary:Star implemented a new geocoding provider, the US Census Geocoder, by adding the necessary Python files and associated reverse geocoding functionality. They developed and integrated APIs to interface with the US Census Geocoder's services. Furthermore, the user addressed code formatting and syntax issues and added test cases to validate the functionality of the newly integrated provider.
Contributions:20 commits, 2 PRs, 17 pushes in 1 month
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