Summary
Megan Moore is a data analyst with nine years of experience blending software engineering and data science to tackle public policy and housing challenges. A University of Chicago MS in Computational Analysis and Public Policy graduate, she has applied machine learning and large-scale data engineering at organizations from Baltimore City DHCD to the Cook County Assessor’s Office and Terner Labs. Her background includes building production-ready tooling for research at IHME and using computer vision and ML in policy-focused research at Stanford RegLab, reflecting a rare mix of high-performance computing, ETL, and applied analytics. Based in Oakland, she focuses on data-driven housing policy and equitable assessment practices, and she often bridges technical and policy teams as a translator and educator. Notably, she has moved codebases to more efficient data libraries and taught AI and Python to public policy cohorts, underscoring a practical commitment to reproducible, impact-oriented analytics.
9 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at New York University Abu Dhabi
Master of Science in Computational Analysis and Public Policy Computer Science & Public Policy, Master of Science in Computational Analysis and Public Policy Computer Science & Public Policy at University of Chicago