Nathaniel is an Assistant Professor of Statistics at the University of Waterloo (UW) in the Department of Statistics and Actuarial Science. From 2015-2018 Nathaniel held a faculty position in the Department of Mathematics and Statistics at the University of San Francisco (USF) where he served as Program Director for the undergraduate data science program. Prior to this, Nathaniel earned BMATH (2010), MMATH (2011) and PhD (2015) degrees in Statistics from the University of Waterloo.

During his career, Nathaniel has also worked as a consultant and instructor within USF's Data Institute and UW's Business and Industrial Statistics Research Group (BISRG). Nathaniel is currently the Director of BISRG. In these capacities he has provided professional training and worked on several projects resulting in collaborations with 20+ organizations. He has also overseen 30+ undergraduate and graduate level data science internships in exploratory data analysis, time series analysis, machine learning, A/B testing and data visualization.

In general Nathaniel is interested in using data to make decisions, solve problems, and improve processes. Specifically, his research interests lie at the intersection of data science and industrial statistics. He is interested in methodological developments in experimental design and A/B testing, process monitoring and network surveillance, reliability and survival analysis, and the assessment and comparison of measurement systems. Recently Nathaniel has developed a family of comparative probability metrics that may be used in place of traditional hypothesis tests in any setting that requires a comparison of statistical quantities. Applications include the comparison of experimental conditions, the comparison of measurement systems, the comparison of survival experiences and more generally, the comparison of expected and predicted response surfaces.

Nathaniel was the recipient of the 2017-2018 Best Reliability Paper in Quality Engineering for the paper titled "Quantifying Similarity in Reliability Surfaces Using the Probability of Agreement".