Andrew Tilley

Spring Labs’ Income Stability Indicators (ISI) leverage data that is not captured by the current credit ecosystem to arm lenders with 300+ forward-looking attributes and risk scores to improve underwriting, acquisition marketing, and portfolio management processes.

I was the lead data scientist and machine learning engineer in the development of ISI, joining Spring Labs at the very beginning of product development.

"Instant Connect" is an experience on HomeAdvisor's website, which enables a homeowner to get connected over the phone to a contractor as they are submitting their service request. I was the lead data scientist on the project and a co-inventor on the patent. My role was to build the models which would determine (1) when it made sense to present the "instant connect" option to the homeowner, and (2) which contractors should be called and in what order. I used a combination of statistical regression and applied mathematics to ultimately improve the contact rate by over 25%.

I built an end-to-end, automated data pipeline and visualization for the tracking of Ibotta's online recommender systems. The program (written in Python and Spark) was completely decoupled from the model-building programs, which meant it provided an unbiased view of real-world model performance. By inventing a new layer of abstraction to compare recommenders to each other, I made the program highly-extensible, and more recommenders could be added with little overhead. I set up a dashboard in DataDog, which was used by machine learning engineers and analytics leaders to assess recommender performance.

I wrote an original assessment of five questions spanning applied mathematics, statistics, and computer programming for prospective data science candidates of Eaze, Inc. The 3-hour assessment was used to filter dozens of candidates, ultimately leading to two highly-successful new team members joining Eaze's analytics team.