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
Predicting New Sort Performance When Data is Sparse / Missing
December 1, 2020
Goal At Wayfair, we want to predict the performance of new sorting algorithms before we launch them. Why? Sorting algorithms make money! Think about it: what’s the main difference between scrolling randomly through our ~18 million products ve...
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...
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.