Nathan Sutton

I engineered the internal R package for my company - this automates routine tasks in our workflow and ensures that our models are both reproducible and statistically sound. I moved our prediction engine from base R to h2o.ai – this allows our data science team to scale up our workflow as we reach more clients with our predictions.

I began my data science training with a M.S. in Ecology and Evolutionary Biology from the University of Tennessee. After working for a year as a research association at Oak Ridge National Lab, I transferred to a full-time data science role at a healthcare startup. I have built my company's data science platform for predictive modeling in R - this ensures our team builds statistically valid, generalizable models.

I am very well versed in the H2O machine learning library, and I have worked to transition my compnay's predictive engine over to this framework. This has allowed us to scale up to larger datasets as well as speeding up training by distributing work to all cores in a machine.