Jay Wang and Jasmine Nettiksimmons explore the business model of Stitch Fix, an emerging startup that uses artificial intelligence and human experts for a personalized shopping experience. Stitch Fix’s service combines recommendation algorithm and human stylists in curating clothes for customers. Jay and Jasmine discuss data collection and feature engineering for the recommendation algorithm, as well as some algorithmic innovations. They then highlight the challenges encountered implementing Stitch Fix’s recommendation algorithm and interacting AI with human stylists before briefly introducing other problems Stitch Fix’s data science team is solving, including language processing, computer vision, inventory simulation, and demand forecasting.
Jianqiang “Jay” Wang is a data science lead at Stitch Fix working on recommendation algorithms and human computer interaction. Previously, Jay worked in academia on survey sampling, nonparametric smoothing, and Bayesian hierarchical models; at HP Labs on demand forecasting and supply-chain management; and as a data scientist at Twitter on ads CTR prediction and ranking. Jay holds a PhD in statistics from Iowa State University.
Jasmine Nettiksimmons is a data scientist at Stitch Fix, where she focuses on robust parameter estimation in observational data and assessing how successfully humans interact with a live recommendation system. Prior to joining Stitch Fix, she worked in the field of cognitive aging with research focusing on biomarker profiles which are predictive of cognitive decline and dementia. In addition to her work in cognitive aging, she has a broad publication record across many public health and social issues including rural health care delivery, childhood obesity, domestic violence prevention, and family-friendly policy usage. Jasmine holds a PhD in epidemiology from UC Davis.
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