Feedback Hub is Wayfair's inhouse web application, built by Data Science Analytics, which enables Analysts all across Wayfair to leverage data and insights generated by Natural Language Models developed by our Voice of the Customer DS team.
4 Min Read
Uplift models seek to predict the incremental value attained in response to a treatment. For example, if we want to know the value of showing an advertisement to someone, typical response models will only tell us that a person is likely to purchase after being given an advertisement, though they may have been likely to purchase already. Uplift models will predict how much more likely they are to purchase after being shown the ad. The most scalable uplift modeling packages to date are theoretically rigorous, but, in practice, they can be prohibitively slow. We have written a Python package, pylift, that implements a transformative method wrapped around scikit-learn to allow for (1) quick implementation of uplift, (2) rigorous uplift evaluation, and (3) an extensible python-based framework for future uplift method implementations.
8 Min Read
A little over a year ago, I found myself taking on a unique and exciting opportunity: Building a Python Platform team from the ground up. Despite its size, Wayfair gives teams a large degree of autonomy in deciding how they operate, which meant we had a true blank slate to start with. Most of my experience has been on product-oriented teams, and this was my first time leading a platform team with purely internal customers. I want to share that journey with you, but first let me give some context around why we needed this team in the first place.
9 Min Read
We run a python/Tornado-based recommendations service behind the scenes at Wayfair. As part of our code deployments, we need to install various third-party libraries to our Tornado servers. The python tools that do this kind of thing are a bit half-baked, so we paper over their inadequacies with puppet.
1 Min Read