Remote Pairing with Strangers
Somewhere in a closet in Texas, a desk sits, isolated from the noise of children and clanging dishes. From here, I gather my wits and connect to the outside world. I reach out to other devs.
Somewhere in a closet in Texas, a desk sits, isolated from the noise of children and clanging dishes. From here, I gather my wits and connect to the outside world. I reach out to other devs.
If there’s one thing we probably have in common, it’s that we both have a “1:1”. Some sort of regular check-in meeting with your manager. If you don’t, I urge you to fix that.
On the algorithms team at Stitch Fix, we aim to give everyone enough autonomy to own and deploy all the code that they write, when and how they want to. This is challenging because the breadth of who is writing micro-services for what, covers a wide spectrum of use cases - from writing services to integrate with engineering applications, e.g. serving styling recommendations, to writing dashboards that consume and display data, to writing internal services to help make all of this function. After looking at the many deployment pipeline options out there, we settled on implementing the immutable server pattern.
Data is embraced as a first class citizen at Stitch Fix. In order to power our complex machine learning algorithms used for styling, inventory management, fix scheduling and many other smart services, it is critical to have a scalable data pipeline implementation. This pipeline must consume and move data efficiently as well as provide low latency, high availability, and visibility.
At Stitch Fix, we build tools that help us to delight our clients, which includes performing the thoughtful research that enables such tools. A great example of this is how we study methods for identifying temporal trends. Consider seasonality, which describes the cyclical patterns in how our client’s preferences change over a year. Identifying seasonal trends requires a mixture of time series analysis and machine learning that is challenging but of critical importance to a fashion retail organization.
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.
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.
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.
Members of our Analytics & Algorithms team are out and about this month – come by and hear us speak!
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.