Sally: At Stitch Fix I work on the inventory forecasting team. Stitch Fix is a retail model where we make recommendations of clothing and accessories that we think clients would like, and then send them a box of five items in it. They decide what they want to keep and then send back what they don't want. There's a lot of interesting data that we can capture. We send things to our clients and then they're sending some back, so we're getting this great feedback cycle. Based on that, we forecast the inventory we're going to need in the future, and we build the process, moving away from traditional retail where everything may have been done in Excel to where we can use machine learning and all the data we collect to anticipate the needs of our clients.

Sally: One example of keeping the human element with the machine learning element is coming from the styling team, the way that we come up with recommendations of clothing that we think would suit each client. We come up with the right list of what we think the client is most likely to keep that is then given to a stylist who will go through and curate a set of five items. There's that human element of knowing what goes with what, and reading the specific requests of a client and having a relationship with that client that is almost impossible to capture with a machine. The combination of the two is what makes it successful. Explaining how we decided to combine automated with human is going to be interesting.

Sally: This is an intermediate talk but I'm trying to make it accessible to everyone. There may be some details where familiarity with Bayes’ and regression would be useful, but in general it should be accessible to everyone.