Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to ship their models independently, with minimal need for handoffs or gatekeeping. To build and maintain production code, a Data Scientist should only have to focus on writing simple Python functions; abstractions built by the Algorithms Platform team can handle the rest. Using functions as a first-class citizen allows Platform to avoid building, maintaining, and enforcing bespoke interfaces. Instead, we focus on enabling the functions Data Scientists write to harness the power of a wide array of platform tools with little to no extra effort on their part. In this talk, we will demonstrate this approach through two case studies -- how Data Scientists at Stitch Fix can rapidly productionalize microservices and develop complex training data pipelines for time-series models.
Elijah ben Izzy has always enjoyed working at the intersection of math and engineering. More recently, he has focused his career on building tools to make data scientists more productive. At Two Sigma, he was building infrastructure to help quantitative researchers efficiently turn ideas into production trading models. At Stitch Fix he is a founding member of the Model Lifecycle team -- a team that focuses on streamlining the experience for data scientists to create and ship machine learning models. In his spare time, he enjoys geeking out about fractals, looking at antique maps, and playing jazz piano.