Thoughtfully writing a blog post

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.

Data Science Interns 2017

This summer our community included four interns, all graduate students who are passionate about applying their academic expertise to help us leverage our rich data to better understand our clients, their preferences, and new trends in the industry. In this blog post you’ll meet the interns, who will tell you a bit about the problems they worked on and the strategies they used to solve them.

Multithreaded in the Wild

See who's out in the wild for this last part of August, and vote for our SXSW proposals.

Understanding Failure Modes in Message and Event-based Systems

In a system based on messages or events, there are numerous ways that system can fail, and the techniques needed to handle those failures are different. You can’t just switch messaging infrastructure or use a framework to address all failure points. It’s also important to understand where failures can occur, even if the underlying infrastructure is perfect. Much like acknowledging that there is no happy path, the way you plan for failure affects product decisions.

Genie in a Box : Making Spark Easy for Stitch Fix Data Scientists

Stitch Fix is a Data Science company that aspires to help you to find the style that you love. Data Science helps us make most of our business and strategic decisions.

Diamond Part II

Announcing Diamond, an open-source project for solving mixed-effects models

Diamond Part I

Solving mixed-effects models efficiently: the math behind Diamond