Thoughtfully writing a blog post

Putting the "Erch" in Merchandising

My team at Stitch Fix builds internal tools for our merchandisers, who are responsible for planning and buying inventory that delights our clients. Internally, we are known as the “Erch” team. It’s a funny name with roots deep in Stitch Fix history, but I’ll save that story for another time. Instead, I have a story of my own to share. Prior to joining the Erch Engineering team, I got my start at Stitch Fix on the Merchandising team.

Don't Get Too Comfortable

It's natural to want to sit next to the people we work with most. Doing so makes pair-coding easier, facilitates conversations that need to happen anyway, and — in general — promotes a certain efficiency.

There's an alternative point of view, though: if people who don't often work closely together sit together, conversations will occur that otherwise would not.

Avoiding Over-Engineering

It’s easy to know if you are under-engineering something, because you produce sloppy work. It’s much harder to know when you’re over-engineering. The root cause of this is expanding the problem at hand so that the solution is much more interesting than the solution to the actual problem.

Multithreaded in the Wild

Members of our Analytics & Algorithms team are out and about this month – come by and hear us speak!

The Skynet Salesman

Here at Stitch Fix we work on a wide and varied set of data science problems. One area that we are heavily involved with is operations. Operations covers a broad range of problems and can involve things like optimizing shipping, allocating items to warehouses, coordinating processes to ensure that our products arrive on time, or optimizing the internal workings of a warehouse.

Data-Driven Fashion Design

A core methodology at Stitch Fix is blending recommendations from machines with judgments of expert humans. Our machines produce recommendations via algorithms operating over structured data, while our human stylists curate and modify these recommendations on the basis of unstructured data and knowledge that isn’t yet reflected in our dataset (e.g., new fashion trends). This helps us choose the best 5 items to offer each client in each fix. The success of this strategy within our styling organization prompts consideration of how machines and humans might be brought together in the realm of fashion design. In this post we describe one implementation of such a system. In particular, we explore how the system could be implemented with respect to a target client segment and season.

Multithreaded in the Wild

Members of our Analytics & Algorithms team are out and about this month – come by and hear us speak!

More Human Humans

Machines are going to take over the world and leave us humans without jobs. This is the meme going around in mainstream business books on the topic of Artificial Intelligence (AI). This is understandable as the number of things that machines can do better than humans is increasing: diagnosing medical conditions, analyzing legal documents, making parole decisions, to name a few. But doing something better doesn’t necessarily make machines an alternative to humans. If machines and humans each contribute differently to a capability, then there is opportunity to combine their unique talents to produce an outcome that is better than either one could achieve on their own. This has real potential to change not only how we work, but also how we understand our experience of being human.

Good Books for All Things Data

One of the greatest benefits of working among a diverse group of data scientists and data engineers at Stitch Fix is how much we can learn from our peers. Usually that means getting ad hoc help with specific questions from the resident expert(s). But it also means getting advice on how best to fill any gaps in our own skill sets or knowledge bases, or just what interesting data science materials to explore in our spare time. Our blog posts usually highlight the former; this post touches on the latter.

Introducing our Hybrid lda2vec Algorithm

The goal of lda2vec is to make volumes of text useful to humans (not machines!) while still keeping the model simple to modify. It learns the powerful word representations in word2vec while jointly constructing human-interpretable LDA document representations.