Dillon Gardner
Dillon is a data scientist with a passion for working on hard, messy problems. He has worked for companies in energy, agtech, and fintech as both an individual contributor and manager. Before starting work in data science, he did his PhD in physics at MIT
Sessions
New data scientists often struggle to make major impacts on solving business problems despite impressive technical skills. A core challenge is the gap between how academics think about performance of models and what matters for a company. As an example, academic work summarizes a model’s receiver operator characteristic (ROC) curve with the area under the curve (AUC). This summary statistic is useless for business applications, which will always have unique trade-offs and constraints. Effective approaches to optimize model performance requires understanding the specific business requirements and how to map that to a well framed data science problem.
In this talk, I will go through a framework of how to think effectively about model trade-offs in terms of maximizing business utility. Through this exercise, we will build intuition for what is required for a model in production to be a success and how to collaborate more effectively with non-technical co-workers.