Summary
Sho Kawano is a PhD candidate in Statistics based in Chicago with a decade of professional experience bridging academia, government, and industry to inform data-driven decision making. He specializes in Bayesian modeling, survey statistics, and model selection, with applied roles spanning public health analytics at the California Department of Health Care Services to quantitative work in the energy sector and a data science internship at Stripe. Known for clear communication, he translates complex probabilistic models into actionable insights for stakeholders outside of statistics. His background in both business development and quantitative analysis gives him a practical appreciation for implementation trade-offs and policy impact. Currently completing doctoral research at UC Santa Cruz after undergraduate studies at UC Berkeley, he combines rigorous theory with real-world problem solving. An uncommon strength is his track record of deploying statistical methods across sectors—public, nonprofit, and private—making him adept at tailoring models to varied operational constraints.
10 years of coding experience
3 years of employment as a software developer
Bachelor of Arts - BA Statistics, Bachelor of Arts - BA Statistics at University of California, Berkeley
University of California Santa Cruz