What We Do
We create model driven experiments to drive business impact. Check out our blog to learn about ongoing projects!
Terabytes of Data Captured Daily
Billion Google Ads per Month
Full Years of Clickstream Data
Million Tagged and Classified Images
Million Products in Catalogue
Million Customer Reviews
Personalization & Recommendations
ML Systems Engineering
Data Science EU
TopShelf: How to Arrange a Page of Products, Not Just Rank Them in a List
May 5, 2020
Many approaches to ranking a list of products or search results are based on assigning a score to each item and sorting in descending order—in other words, greedy sorting approaches. In e-commerce, predictive models place the product that the custo...
The Visual Complements Model (ViCs): Complementary Product Recommendations From Visual Cues
March 16, 2020
Introduction Creating a recommendation system for home decoration poses unique challenges: each customer has his/her own taste and would like to maintain a cohesive personal style across his/her home. We at Wayfair know that it is hard to describe t...
Hoover: How to enable data scientists to stop managing ETL pipelines and get back to doing data science
February 11, 2020
Summary Business moves fast and data science runways are long. What can we do to remove friction and iterate on data science models as quickly as possible? In this blog post, we describe how Wayfair builds Jupyter-based tooling that empowers data sc...
Who We Are
We are a diverse group of analytical problem solvers. And we might be more than a little nerdy.
Director of Data Science
University of Chicago
Head of Pricing
Head of DS Merchandising
PhD Operations Research
Head of DS Marketing
MBA Finance & Entrepreneurship
Head of Personalization & Recommendations
Head of Computer Vision
PhD Electrical Engineering
Head of Operations Research
MS Operations Research
Head of ML Systems Engineering
MBA Marketing & E-Commerce
University of Rochester
Head of Data Science EU
Humboldt University of Berlin
How We Work
Here at Wayfair Data Science, our core values are rigor, curiosity, and fun.
From search engines to shipping logistics, Data Science is central to everything we do at Wayfair. As such, it's vital that we get things right. Our thorough in-house testing platform, rolling code deployments, and iterative research-style approach ensure that our work is thorough, precise, and driving the business.
Wayfair’s abundance of data means that there are always new problems to be solved. As such, team members are empowered to explore our data to develop new, high impact ideas and then develop experiments to probe those questions. We also partner with professors and students from Harvard, MIT, Stanford, Columbia, and Cornell to bring the most advanced ML technologies into production.
No one is too busy to brainstorm or answer a question, and code is shared as freely as the snacks in the kitchen. We like doing things together, whether that means collaborating on a pricing experiment or going rockclimbing, kayaking, painting, or visiting a distillery with our coworkers during our monthly pod outings.