Tyler Smith is a data scientist with 13 years of professional experience in maintaining complex data sets and applying a wide variety of statistical and machine learning algorithms using Python, R, and SQL. Trained as an epidemiologist, Tyler is expert at sharpening ambiguous research questions, developing scalable and reproducible data analysis workflows, visualizing data, and reporting actionable results to decision-makers. He is comfortable working with leaders and stakeholders at all levels and communicating with technical and non-technical audiences.

As a postdoctoral fellow, Tyler is studying how environmental exposures during pregnancy and childhood can alter health and developmental trajectories. 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.