Tyler Smith is a data scientist with 13 years of professional experience. He is an expert at building and analyzing large data sets using Python, R, and SQL, visualizing results, and reporting actionable information to decision-makers. He enjoys partnering with organizational leaders and communicating with technical and non-technical audiences alike.

Trained as an epidemiologist, Tyler studies how environmental exposures during pregnancy and childhood can alter health and developmental trajectories. His current research focuses on air pollution mixtures, folate metabolism in pregnancy, and child neurodevelopment. His doctoral research aimed to estimate the effects of potential interventions to reduce drinking water arsenic during pregnancy on immune and respiratory outcomes among infants in rural northern Bangladesh. More generally, he is interested in combining rigorous study designs and novel statistical and machine learning methods to estimate causal effects using observational data.

Tyler holds a PhD in Exposure Science and Environmental Epidemiology and an MPH in Epidemiologic and Biostatistical Methods, both from Johns Hopkins. Before the doctoral program, he worked in environmental policy. Tyler was born and raised in Seattle. When he’s not working, he can be found hiking, skiing, or otherwise engaged on the side of a mountain.