Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. Data Scientists are expected to build their systems end to end and maintain them in the long run. We rely on automation, documentation, and collaboration to enable data scientist to build and maintain production services. In this talk I will discuss what we have built and how we communicate about these tools with our data scientists.
Juliet Hougland is a data scientist at Cloudera, and contributor/committer/maintainer for the Sparkling Pandas project. Her commercial applications of data science include developing predictive maintenance models for oil and gas pipelines at Deep Signal, and designing/building a platform for real-time model application, data storage, and model building at WibiData. Juliet was the technical editor for Learning Spark by Karau et al. and Advanced Analytics with Spark by Ryza et al. She holds an M.S. in applied mathematics from the University of Colorado, Boulder and graduated Phi Beta Kappa from Reed College with a BA in math-physics.