Here is a quick Q/A session with Marcos Sainz, a fellow CMUer who is data scientist at ModCloth. ModCloth has an office in Pittsburgh and was one of the companies who attended our July workshop. I did the unfortunate mistake of showing my wife their website, an error which immediately cost me 200$.. :-)
What is the special market / what make modcloth unique?
ModCloth’s mission is to democratize the fashion industry. We seek to do so by empowering our community of shoppers through a social commerce platform that brings products to market with customer feedback and validation. Programs like Be the Buyer™ allow customers to vote items from emerging designers into production. ModCloth has built a loyal community through engaging, interactive contests on our blog and an active involvement in social networks such as Twitter and Facebook, and we’re gaining even more attention for our use of new social platforms, such as Instagram and Pinterest.What is the size of the company? where are you based?
ModCloth was founded in 2002 when Eric & Susan Koger were only 18 and 17 respectively. In just several years, we've grown from our humble beginnings in a Carnegie Mellon University dorm room to “America’s Fastest-Growing Retailer,” with offices in Pittsburgh, San Francisco, and Los Angeles. In 4 years, ModCloth has grown from 3 to over 300 employees (and growing)!
ModCloth has more than 500,000 fans on Facebook, over 80,000 followers on Twitter, works with over 700 independent designers, and averages over 500 product reviews daily. Our Be the Buyer™ program has received over 11 million votes so far.What are some interesting machine learning challenges you face?
A particularly noteworthy challenge we face is the "cold start" problem as it relates to our lean and ever-changing supply chain. In plain words, by the time we have gathered enough data about any given product that we sell on our site, it is too late to use that data for inference and prediction for that product. To make things more exciting, mix in the fact that fashion is a relatively volatile and difficult-to-quantify concept. Change is the only constant.Would you like to share anything about your solution methods / mode of work ?
Central to the success of some of our Data Science initiatives has been the creation of a Data Warehouse or centralized repository of data originating from multiple systems. Having all or most relevant data in one place in an easily-consumable format makes the life of data analysts/scientists much easier.