David Grayson

Hi! I am an experienced data scientist and ML engineer who has worked at large and small companies covering software tech, biotech, and utilities. Before working in industry I received my PhD in neuroscience where I published papers on computational neuroimaging. A summary of my industry experience: 1) I designed and built real-time ML apps for Intuit's QuickBooks, improving the product experience for over a million users. 2) led some cool projects at a biotech startup predicting neurological disease using computer vision, analytics, and ML. 3) Most recently I have been working as a technical lead in the utilities space, helping teams of data scientists and engineers to implement predictive maintenance using machine learning and computer vision.

As a mentor I like to focus on mentees on gaining mastery of the tools and approaches needed to build and manage a machine learning lifecycle end-to-end. For me, this covers three main skillsets: statistical competency, programming ability, and developer best practices. With those foundations, we then move to understand the stages of the lifecycle including business understanding, EDA, data cleaning, feature engineering, pipelineing, training, optimizing, deploying, testing, and monitoring. Finally, my philosophy is that the best learning comes from doing!