Summary
Merritt Smith is a PhD candidate at UC Berkeley with eight years of applied data science and ML experience focused on public policy and social impact. They have built large-scale models—from estimating populations across African municipalities to predictive imputation for federal surveys—and used causal inference and ML to tackle domestic violence, police misconduct, and gun violence. Merritt has bridged academia and practice through research and teaching roles at the University of Chicago and LSE, and has built production-ready anomaly detection and decision-process frameworks in compliance and public-sector settings. Comfortable coding in Python and R and translating methods for policy audiences, they combine rigorous causal thinking with engineering discipline to deliver measurable outcomes. An early maker of novel analytical pipelines, Merritt’s work often pairs machine learning with concrete institutional deployment rather than purely theoretical contributions.
8 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Information Science/Studies, Doctor of Philosophy - PhD, Information Science/Studies at University of California, Berkeley
Bachelor of Arts (BA), Data Science and Public Policy, Bachelor of Arts (BA), Data Science and Public Policy at Tufts University
Master of Science - MS, Computational Analysis and Public Policy, Master of Science - MS, Computational Analysis and Public Policy at Harris School of Public Policy at the University of Chicago
4.2/4.0, 4.2/4.0 at Pacific Collegiate School
London School of Economics and Political Science
Spanish