Steven Scott is an expert in Bayesian statistics, forecasting, multi-armed bandit experiments, latent variable modeling, hierarchical models, finite mixture models, hidden Markov models, and model selection. He graduated summa cum laude in Mathematics and Economics from Texas Christian University. He furthered his education at Harvard University where he obtained his A.M. and a Ph.D. in Statistics.
He was an Assistant Professor of Statistics at University of California’s Marshall School of Business from 1998 to 2007 and served as the Director of Statistical Analysis at Capital One Financial Corporation from 2007 to 2008. From 2008 t0 2018, he worked as a Senior Economic Analyst and Director of Statistics Research at Google.
As Senior Analyst and Director of Statistics Research at Google, Steven Scott used his expertise to further the web engine’s statistics research. One of his significant accomplishments at the tech company involves an early, accessible paper on Thompson sampling for multi-armed bandits that gained substantial traction both inside and outside Google. His study led to MAB adoption in parts of the Google ad system and has spurred further research at Facebook, Microsoft, Amazon, and elsewhere. He also wrote the code for the mathematical back end of the system that served multi-armed bandit experiments for third-party website optimization.
He was an Assistant Professor of Statistics at University of California’s Marshall School of Business from 1998 to 2007 and served as the Director of Statistical Analysis at Capital One Financial Corporation from 2007 to 2008. From 2008 t0 2018, he worked as a Senior Economic Analyst and Director of Statistics Research at Google.
As Senior Analyst and Director of Statistics Research at Google, Steven Scott used his expertise to further the web engine’s statistics research. One of his significant accomplishments at the tech company involves an early, accessible paper on Thompson sampling for multi-armed bandits that gained substantial traction both inside and outside Google. His study led to MAB adoption in parts of the Google ad system and has spurred further research at Facebook, Microsoft, Amazon, and elsewhere. He also wrote the code for the mathematical back end of the system that served multi-armed bandit experiments for third-party website optimization.
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