Operationalizing Data Science: How I Became an Engineer to Avoid Engineering

Join Jeff Keller as he recounts his journey from the safe—but limited—waters of an R-only workflow to one that enables controlled cloud deployment of numerous and scalable predictive models, while maintaining scientific integrity and rebuffing the tyranny of Minimum Viable Product. We’ll take a look at the tools and processes Jeff created to achieve this in an ever changing, budget-less environment with only the intermittent sympathies of a few architects who understood the potential of well-executed data science.

Also you don't have to wait until March to see a great data science talk like this! Consider our Winter Party with rapid fire talks later this month on December 19th (https://www.meetup.com/Burlington-Data-Scientists/events/256147304/)

About Jeff
Jeff Keller is a data scientist with an M.S. in Applied Statistics from Penn State and degrees in Mathematics and Economics from UVM. A Burlington native, he’s been doing data science for 10 years starting with RSG and now with Cox Automotive (Dealer.com). Jeff has always been drawn to the practical application of mathematics and statistics, but is happy to get down with the underlying calculus if needed!