Thoughtfully writing a blog post

What it's like to be a developer at Stitch Fix

Recently Increment published an issue about what it is like being a developer at a number of great companies like Fastly, Lyft, and DigitalOcean. We thought it would be fun to create a blog post answering the same questions about what it’s like at Stitch Fix.

Multithreaded in the Wild

See who's out in the wild for November

Word Tensors

Counting and tensor decompositions are elegant and straightforward techniques. But these methods are grossly underepresented in business contexts. In this post we factorized an example made up of word skipgrams occurring within documents to arrive at word and document vectors simultaneously. This kind of analysis is effective, simple, and yields powerful concepts.

Stop Using word2vec

When I started playing with word2vec four years ago I needed (and luckily had) tons of supercomputer time. But because of advances in our understanding of word2vec, computing word vectors now takes fifteen minutes on a single run-of-the-mill computer with standard numerical libraries1. Word vectors are awesome but you don't need a neural network -- and definitely don't need deep learning -- to find them. So if you're using word vectors and aren't gunning for state of the art or a paper publication then stop using word2vec.

NBA Season Kickoff

Today is the start of the 2017-2018 NBA Season. Basketball statistics have become a rich and intriguing domain of study, bringing new insights and advantages to the teams that embrace such empiricism. Of course, the framing and analytic techniques used to study basketball are generalizations - they also give intuition to problems in business or other domains (and vice versa). So, for all the basketball statistics enthusiasts out there, as well as those that are looking for inspirations for their own analytic challenges, we thought we’d share a compendium of our past basketball-related posts.

Multithreaded in the Wild

See who's out in the wild for October

Internal Software: Internal Software and Data Science

At Stitch Fix we certainly have enough data that it qualifies as Big, but since we collect the data ourselves we focus on making it as Rich as possible.

Time Dependent Classification

In this post we’ll take a look at how we can model classification prediction with non-constant, time-varying coefficients. There are many ways to deal with time dependence, including Bayesian dynamic models (aka "state space" models), and random effects models. Each type of model captures the time dependence from a different angle; we will keep things simple and look at a time-varying logistic regression that is defined within a regularization framework. We found it quite intuitive, easy to implement, and observed good performances using this model.

Multithreaded in the Wild

See who's out in the wild for September

The curious connection between warehouse maps, movie recommendations, and structural biology

Here at Stitch Fix, we work on many fun and interesting areas of Data Science. One of the more unusual ones is drawing maps, specifically internal layouts of warehouses. These maps are extremely useful for simulating and optimising operational processes. In this post, we'll explore how we are combining ideas from recommender systems and structural biology to automatically draw layouts and track when they change.