Luke Smith is a data scientist and PhD-trained statistician with over 15 years of experience building statistical and ML solutions for experimentation and offline analysis, now at GrowthBook after a multi-year research scientist career at Amazon. He specializes in causal inference, experiment design, and data visualization, and has led cross-organizational initiatives to productionize science ownership and improve platform operations. A hands-on R and Python practitioner, he has contributed statistical methods—quantile estimation, Bayesian A/B testing, and power analysis—to the open-source GrowthBook feature-flagging and A/B testing platform. Colleagues rely on him to translate customer needs into robust, production-ready analytics and to raise experimentation standards across teams.
9 years of coding experience
13 years of employment as a software developer
PhD, Statistics, PhD, Statistics at North Carolina State University
Open Source Feature Flagging and A/B Testing Platform
Role in this project:
Data Scientist
Contributions:164 reviews, 79 PRs, 202 pushes in 1 year 1 month
Contributions summary:Luke primarily focused on implementing and refining statistical methods within the GrowthBook platform. Their work involved quantile estimation, Bayesian A/B testing, and power analysis. They contributed code for new statistical tests, improved existing methods, and addressed numerical errors. Additionally, the user made contributions related to documentation and testing of the statistical features.
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