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Wayfair interview notes, page 2 of 2

Large Enterprises

Screna has 13 Wayfair interview write-ups. The most reported round is the product sense round (6).

13 interview notes · updated Jul 2026

Wayfair·Software Engineer·Onsite - Product Sense / Strategy

Jun 2026
Wayfair product analytics case focused entirely on a paid reviews ROI problem. Pretty math-heavy for what felt like a strategy question at first glance.
  • Wayfair has 100 products with no reviews. Data suggests that 5 reviews per product lifts sales meaningfully. If you were to pay customers for reviews, how much should you pay per review, assuming you want to break even within 6 months? Assume average product price is $200, gross margin is 20%, and conversion rate goes from 2% to 3%.

“I went straight to the math and kind of forgot to frame the problem first, which I think hurt me.”

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Wayfair·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Round 2 at Wayfair for an MLE role was a case study focused on Google Search bidding strategy and price calculation. Pretty applied, less about modeling and more about business logic.
  • Walk through how you would approach bidding on Google Search ads, and how you would calculate the optimal bid price.

“This caught me somewhere between a pricing problem and an ML problem and I wasn't sure which direction they wanted.”

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Wayfair·Data Scientist·Online Assessment (OA)

Jun 2026
Wayfair data scientist OA with two tasks back to back, SQL and Python. The SQL one had a tricky time-window filter that I almost misread, and the Python function had more edge cases than I expected for something that looked straightforward on the surface.
  • Given a customers table and a purchases table, write a SQL query to find the customer(s) with the highest purchase price within the earliest 10-year window of purchase history. Return all tied customers if applicable.
  • Write a Python function using pandas that cleans a student scores DataFrame by dropping students missing two or more scores, imputing remaining nulls with per-column medians, then returning the top 5 students sorted by math score descending, physics score descending, and student ID ascending.

“The 10-year window thing tripped me up.”

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