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Developing New Features With Our Customers

Emma Colner on

One of the great things about being a software engineer at Stitch Fix is that most of our applications are internal-facing. This means that many of our “customers” are also our co-workers and may even work in the same building as us. For example, I work on our engineering team that supports our merchandising organization - the people who plan, buy, design, and allocate our inventory. Our merchants work with us in our headquarters on Market St. in downtown San Francisco.

iOS Code Signing - Part 1

Flora Lee on

Update (10/31/2016): We’ve written a newer blog post about how we test, integrate and deploy our iOS app. It complements the information here, and includes up-to-date details about our current process.

Strength in Numbers: Why Golden State Deserved to Win it All

Joerg Fritz and Kim Larsen on

The Golden State Warriors won the NBA finals last year and posted the best record in the regular season. Yet some people have argued that their success was based on luck and that playing “small ball” is a recipe that only works in the regular season.

Simple Rails APIs with Stitches

Dave Copeland on

We don’t have a single monolithic application—we have lots of special purpose applications. Initially, there were just a few, managed by a few developers, and we used RubyGems to share logic between them. Now, we have over 33 developers, are a much bigger business, and have a lot more code in production. So, we’ve turned to HTTP services. Instead of detailing the virtues of this architecture, I want to talk about how we do this with Rails using a shared gem that’s less than 1,000 lines of code called stitches.

Multiple Hypothesis Testing

Sven Schmit on

In recent years, there has been a lot of attention on hypothesis testing and so-called “p-hacking”, or misusing statistical methods to obtain more “significant” results. Rightly so: For example, we spend millions of dollars on medical research, and we don’t want to waste our time and money, pursuing false leads caused by flaky statistics. But even if all of our assumptions are met and our data collection is flawless, it’s not always easy to get the statistics right; there are still quite a few subtleties that we need to be aware of.

An Introduction to Linear Models for Data Science

Brad Klingenberg on

Data science is a new field, and it isn’t always obvious what makes a good data scientist. What should they know? Tools, frameworks, and technologies are always changing. In the midst of this shifting landscape data scientists can differentiate themselves by mastering one of the most useful tools from applied statistics: linear modeling. Last week I spoke to the latest class of fellows at Insight about this very topic and the slides from my talk can be found here.

Testing Required, TDD Optional

Nick Reavill on

I’ve been talking to and interviewing quite a few junior software engineers and Rails bootcamp graduates of late. Since testing is so central to our work here at Stitch Fix I usually ask them about it and TDD. Often people respond sheepishly that they don’t really write tests first but they know they should. One person told me that their bootcamp class read the “TDD is dead. Long Live Testing” post by DHH and took away the lesson that testing wasn’t required. I’m pretty much 100% certain that’s not the correct interpretation. Perhaps it points to a problem with TDD in Rails development culture: testing is a requirement and TDD is a choice, but the two are often conflated.

Rapid Development & Performance in Spark For Data Scientists

Jas Khela on

Spark is a cluster computing framework that can significantly increase the efficiency and capabilities of a data scientist’s workflow when dealing with distributed data. However, deciding which of its many modules, features and options are appropriate for a given problem can be cumbersome. Our experience at Stitch Fix has shown that these decisions can have a large impact on development time and performance. This post will discuss strategies at each stage of the data processing workflow which data scientists new to Spark should consider employing for high productivity development on big data.

Quantifying my Transition from Academia to Data Science

Alex Smolyanskaya on

When I made the change from my postdoc role in neuroscience to data science at Stitch Fix, I was hoping to get to do more of the thing I loved most about being a scientist: collaborative data-driven discovery. That’s exactly what I got, and I have the data to prove it.

Deep Style: Inferring the Unknown to Predict the Future of Fashion

TJ Torres on

Here at Stitch Fix, we are always looking for new ways to improve our client experience. On the algorithms side that means helping our stylists to make better fixes through a robust recommendation system. With that in mind, one path to better recommendations involves creating an automated process to understand and quantify the style our inventory and clients at a fundamental level. Few would doubt that fashion is primarily a visual art form, so in order to achieve this goal we must first develop a way to interpret the style within images of clothing. In this post we’ll look specifically at how to build an automated process using photographs of clothing to quantify the style of some of items in our collection. Then we will use this style model to make new computer generated clothing like the image to the right.