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September 10, 2021
When a Wayfair customer shops for a product that is just right for her home, color plays a big role. For example, a customer might say, “I am looking for a green chair.” Or she might say, “I am looking for a teal chair.” There may be various versions of these customer color search stories. As data scientists at Wayfair, we want to solve these customer problems by algorithmically extracting the color information from the products and assigning customer-friendly color names.
August 23, 2021
The Case for Module Federation in Partner Home
August 19, 2021
In this blog post, you’ll get an inside look at our Wayfair CastleGate Logistics team which develops and designs the technology that enables us to get over 22 million products from the manufacturer to our customers’ homes. This will be the first in a series of posts focused on our Logistics Tech team, providing an insight into our challenges, what drives/motivates us, and how we work at Wayfair! Follow along to learn more about what it’s like to work at Wayfair!
August 19, 2021
When you think "Kubernetes," the first word that comes to mind probably isn't "frontend." Here at Wayfair our frontend infrastructure is heavily integrated with Kubernetes, including a toolkit that generates frontend apps with Kubernetes integration out of the box.
August 11, 2021
If you are among the tens of millions of customers who have shopped at Wayfair, you have experienced one of the many technologies built by the Machine Learning and Data Platforms team. We serve the business, engineering and data science teams across Wayfair, who are working on solving one of the most intriguing and gratifying challenges — helping our customers make their dream home a reality.
July 30, 2021
How data scientists at Wayfair build scalable ML systems to programmatically optimize marketing decisions.
July 26, 2021
Wayfair hasn’t always been a household name, but technology has always been at the center of everything we do. When co-founders Niraj Shah and Steve Conine built our first website, racksandstands.com, they did so with ambitious plans for the future. As they built more than 250 more sites, they used data to understand how best to scale their effort to provide a better shopping experience across various Home categories.
July 22, 2021
In earlier installments we covered how our cloud journey started, talked about testing and validation, and discussed internal processes that positioned us for making rapid iterations and helped to achieve predictable delivery.