Thought Experiments in the Browser

As data scientists, we work in concert with other members of an organization with the goal of making better decisions. This often involves finding trends and anomalies in historical data to guide future action. But in some cases, the best aid to decision-making is less about finding “the answer” in the data and more about developing a deeper understanding of the underlying problem. In this post we will focus another tool that is often overlooked: interactive simulations through the means of agent based modeling.

Machine Learning To Kickstart Human Training

Stitch Fix values the input of both human experts and computer algorithms in our styling process. As we’ve pointed out before, this approach has a lot of benefits and so it’s no surprise that more and more technologies (like Tesla’s self-driving cars, Facebook’s chat bot, and Wise.io’s augmented customer service) are also marrying computer and human workforces. Interest has been rising in how to optimize this type of hybrid algorithm. At Stitch Fix we have realized that well-trained humans are just as important for this as well-trained machines.

The Merchandising Calendar

Since Stitch Fix is a retail company at heart, we operate on the merchandising calendar. The merchandising calendar is used by the retail industry for accounting of sales, inventory and payroll. It originated in the 1930s and became widely adopted by the 1940s. The primary reason for its creation was to guarantee the same number of weekends in comparable months since a large percentage of retail sales occur on weekends. Also the calendar ensures that any end date of a period falls on the same day of the week.

Assessing the Null Hypothesis — A Meta-Analysis (Ruminations on April 1st)

As statisticians and data scientists, we often set out to test the null hypothesis. We acquire some data, apply some statistical tests, and see what the p-value is. If we find a sufficiently-low p-value, we reject the null hypothesis, which is commonly referred to as \(H_0\).

Data Science at Stitch Fix

Over the last couple of years, Stitch Fix has amassed one of the more impressive data science teams around. The team has grown to 65 people, collaborates with all areas of the business, and has a well-respected data science blog plus several open source contributions.

As a member of this team since late 2014, and someone who has spent 15 years in the analytics space prior to that, I’ve often reflected on how the data science team at Stitch Fix got to this point. Is it attributable to our business model? Or, is Stitch Fix doing something differently when it comes to growing and managing its data science team?

The short answer is that the business model does provide a fertile environment for data science. However, it goes deeper than that: the approach to managing and building data science teams at Stitch Fix is unique in many ways. In fact, it has debunked many of the beliefs I held about management and growth prior to joining the team.

Despite machines taking over the world, humans still prove useful

We now rely on algorithms to tell us what movies to watch, what cat food to buy, and we’re even starting to let them drive our cars. That said, there’s still something a little odd about an algorithm picking out a dress for your date on Saturday night or the perfect tie for your best friend’s wedding. The simple fact is that computers can do a lot these days, but while their capabilities continue to develop there are still many things that humans do better.

Debunking Narrative Fallacies with Empirically-Justified Explanations

When we experience volatility in business metrics we tend to grasp for explanations. We fall for availability bias, and the more visceral or intuitive the explanation the quicker we latch on. ‘The cool weather is dissuading customers’, ‘customers are happier on Fridays because the weekend is coming’, ‘people are concerned with the economic downturn’, ‘competitor xyz is making a lot of noise in the market which is diluting our messaging’, … etc. The list goes on and on.

Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department

“What is the relationship like between your team and the data scientists?” This is, without a doubt, the question I’m most frequently asked when conducting interviews for data platform engineers. It’s a fine question – one that, given the state of engineering jobs in the data space, is essential to ask as part of doing due diligence in evaluating new opportunities. I’m always happy to answer. But I wish I didn’t have to, because this a question that is motivated by skepticism and fear.

Can We Get A Download Button? (Pyxley Update)

The Algorithms team is deeply embedded in every aspect of Stitch Fix, providing insights and recommendations to help our business partners make data-driven decisions. Pyxley was born out of the need to deliver those insights without spending a lot of time on front-end design. The original plan with Pyxley was to start off with a small set of simple components and then add new components into the package as they were developed for various dashboards. Unfortunately there was one fatal flaw in that plan: our team loves Tables. Sortable tables, tables with two headers, and even tables within tables. Therefore, despite having built several dashboards, there hasn’t been a need to increase the set of components.

The Postmodern Tailor: Size Personalization Beyond Labels

“What’s your size?”