Wayfair’s investment in Data Science has been a core driver of our success. Our proprietary algorithms and unique depth of data in the home goods category help us to determine quickly and objectively how we can enhance the experience of our customers and suppliers.

- Steve Conine,
Co-Founder and Co-Chairman

Our Values

Wayfair is a 6 billion dollar company intent on revolutionizing the 800 billion dollar home goods industry: we means business. We hire the best people, we give you the runway, we innovate together, and we hold the bar high. In Wayfair Data Science, we achieve this by focusing on 4 things: rigor, curiosity, fun, and transparency.


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 to design 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 rock climbing, kayaking, painting, or visiting a distillery with our coworkers during our monthly pod outings.


At Wayfair, we go beyond the quarterly report to keep our teams informed about the business. We pride ourselves on maintaining transparency and a horizontal power structure across the company. As such, our leaders are not siloed off in corner offices—they work right alongside interns and entry level employees, ready to discuss projects, answer questions, and engage in collaborative problem solving.

Humans of Wayfair DS

John Walk

Nuclear Physicist. Ice Climber.

"In physics research, you kind of lock yourself in a room for 18 months and come out with a perfect paper that 12 people will read, because plasma physics is not a large field. Whereas as a Data Scientist you can get something with real impact and pure results in a quick turnaround."

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Jen Wang

Chemist. Traditional Chinese Medicine Enthusiast.

"In the 21st century the way we do research is changing. In the past, yes, academia was the best place to do research. But now there are startups, research units in industry, and all sorts of opportunities."

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Hussain Karimi

Mathematical Oceanographer. Wrestler.

"Our team is pretty open about exploring new ideas. To a degree we do the 'throw something at the window and see what sticks' approach. I feel like that is something unique to Wayfair and is even more pronounced in Data Science here: We would rather experiment and fail than do the same thing over and over again."

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Learn More

Moving from Academia to Industry: 10 Tips from Wayfair Data Science PhDs

October 31, 2018

So, you graduated college, spent half a decade in a quantitative PhD program, maybe took a postdoctoral fellowship….and now what? You may want to stay the course and pursue a tenure track faculty position at a university–or, you may not. While th...

Diversity in Tech: Data Science Manager Jen Wang Shares Her Thoughts with ForbesBooks

August 13, 2018

Workplace diversity is not just an ethical issue. Teams that overcome diversity issues (e.g., gender, race, ethnicity, experience) often achieve greater productivity than homogeneous teams. If you’re surrounded by people who are different from you,...

Algo Dev Day: A Community Service Hackathon

May 16, 2018

Earlier this month, the Pricing Algorithms team had a Community Service Hack Day here at our office, dedicating an afternoon to work on simple tools that could be used as part of any project. The day was an opportunity to fill any knowledge gaps in h...