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Interview experiences · The New York Times

The New York Times Data Analyst Interview Experience — Five Rounds, Three References, Then Rejected

RejectedOnsiteInterviewed 2026-01

It's been four years since this interview, but on top of wishing everyone a happy new year, I wanted to share roughly what the interview process at this company was like. I went through five rounds of interviews with this company in total — the process was pretty drawn out. At the end they also wanted three references (a peer, my direct manager, and a manager I had collaborated with). After going…

It's been four years since this interview, but on top of wishing everyone a happy new year, I wanted to share roughly what the interview process at this company was like.

I went through five rounds of interviews with this company in total — the process was pretty drawn out. At the end they also wanted three references (a peer, my direct manager, and a manager I had collaborated with). After going through a lot of trouble to line those up, they told me they'd gone with someone else. Damn it all.

It started with NYT's own in-house recruiter reaching out to me on LinkedIn. The role itself wasn't very clearly defined. After interviewing with a senior analyst, a project manager, a senior manager, a director, and an executive director, I realized the job was full of miscellaneous tasks. The interviews covered coding, A/B testing, scenario-based problems, and the usual "do you have any questions for me."

For the coding portion I referenced some previous interview reports I'd found online. At the time, they were still using Google Cloud.

  1. Find the unique content views for mobile (phone + tablet) versus desktop — is there a difference between the two view counts? (I used CASE WHEN plus COUNT DISTINCT(content_id).)
  2. What are the peak usage hours for the two device types? You need the hour function on the timestamp. Before the interview they send you a syntax reference for working with dates/times, and it's worth reading carefully.
  3. How many unique pieces of content were viewed, and how many had null values? Same idea again — COUNT DISTINCT plus COUNT(id) WHERE IS NULL.
  4. What's the distribution of content types for the first URL each agent viewed each day?

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