School of Business at the University of Southern California. Between USC and Google he also had a brief tenure at Capital One, where he was a Director of Statistical Analysis.
Dr. Scott is a Bayesian statistician specializing in Monte Carlo computation. In his academic life he has written papers on Bayesian methods for hidden Markov models, multinomial logistic regression, item response models, support vector machines. These methods have been applied to network intrusion detection, web traffic modeling, educational testing, health state monitoring, and brand choice, among others.
Since joining Google he has focused on models for time series with many contemporaneous predictors, on scalable Monte Carlo computation, and on Bayesian methods for the multi-armed bandit problem.