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      Data Scientist, Product Analytics Interview

      8 Jun 2015
      Anonymous interview candidate
      Menlo Park, CA
      No offer
      Positive experience
      Difficult interview

      Application

      I applied through a recruiter. The process took 3 weeks. I interviewed at Meta (Menlo Park, CA) in Apr 2015

      Interview

      A recruiter reached out to me on LinkedIn. I had a quick call with her to confirm my interest and then she set me up with a technical phone screen that same week. Because I did okay, not great, I had a second phone screen the following week. Then within another week or two I went in for an onsite interview with 5 different team members ranging from analyst to manager. The interview was on a Friday and by Tuesday, I believe, they had gotten back to me with a no - but they softened the blow by letting me know that I was a good fit, but that my technical skills weren't quite there, so I should give them a call back in 6 months or when they are there, whichever comes last. Overall the people I interviewed with were very cool and supportive (one exception, but there always is, isn't there?) In the end, I don't actually think I was qualified for the position, but had I been, I most likely would have taken the role. The only negative for me really was the workspace - the entire office is one room. Both phone screens were the same format. They start by telling you about the role, ask you to describe your experience and then they jump right into a case, which also serves as a SQL exercise. They provide you with a sample schema then ask you to write queries against it and answer some strategic questions about how you might think about the output or what conclusions you could draw from the metrics available to you (or those you might create.) Overall for someone who uses SQL regularly (which I do) this was very easy - though I will say if you are not familiar with the format, where you code live (which I wasn't) it can throw you off. All of the queries I saw in the phone screen and the onsite interview relied a lot on COUNT and self joins and other nested queries, so if you feel you might be nervous you could review those concepts. I was really happy with the onsite interview, it was even a little bit fun, but I really didn't know what I was doing half the time, so that was a bit awkward. What I liked the most was that it was almost 100% skill based, none of the "tell me about a time when" or "what's your greatest strength" nonsense that I'm used to. The first interview was a business case. I was asked what kind of metrics I would look at, how I would track them, what conclusions I might draw from different scenarios. It was basic, but they were looking for some specific insights so I would say even though it was open ended, there were right and wrong answers. The next interview was a SQL interview. It was a lot like the phone screen, just more involved. The following interview was Python/R based and asked me to write a couple of algorithms. I don't actually know Python/R so I did pretty badly! But it's a testament to the culture (or at least the kindness of this one guy) that he was not rude about it, just tried to give me some coaching and be friendly while I wrote a lot of non-sensical things on the whiteboard. Since then I have taken Coursera's R Programming course and I would say that if you pass that class you should be able to pass this interview - though perhaps if you fly through the first couple of algorithms they get progressively harder in which case the class may not be sufficient. The next interview was a probability question. I had done a ton of probability prep based on the reviews I saw on Glassdoor but he really knocked me over with a question that I had no idea how to answer. Bayes rule what? That was not at all relevant in this case, sadly. The final interview was another case, or so it seemed, but actually I think it was more of a test of my stats 101 knowledge, which I didn't quite pick up on right away. The interviewer asked me a kind of confusing question which I thought was a bit of an analysis question, but really he was asking me: "what is standard deviation?" but in the most round about, muddled way possible. He also had a thick accent and was very stern which didn't help. So I actually think I did the worst on this (even worse than the Python/R which I didn't even know!), which is funny because I know stats very well, but that's how it goes. Overall I would say I was very unfamiliar with this style of interview. I have an MBA so I had done a lot of less technical interview prep. However it seemed very fair and very manageable... if you actually know the material.

      Interview questions [1]

      Question 1

      You're at a casino with two dice, if you roll a 5 you win, and get paid $10. What is your expected payout? If you play until you win (however long that takes) then stop, what is your expected payout?
      2 Answers
      45

      Other Data Scientist, Product Analytics interview reviews for Meta

      Data Science - Product Analytics Interview

      21 Apr 2026
      Anonymous interview candidate
      Menlo Park, CA
      No offer
      Neutral experience
      Average interview

      Application

      I interviewed at Meta (Menlo Park, CA)

      Interview

      Started with a recruiter/screening round and then moved into the full loop. The full loop had four rounds focused on analytical execution, analytical reasoning, technical skills, and behavioral. SQL was a major part of the process, along with product metrics, experiments, stats, and communication. The process was structured but still challenging because the questions were open-ended and required clear thinking, not just memorized answers.

      Data Scientist, Product Analytics Interview

      9 Mar 2026
      Anonymous interview candidate
      No offer
      Positive experience
      Average interview

      Application

      I interviewed at Meta

      Interview

      Recruiter screening then 1 hr technical screen on sql and business case. The interviews were focused heavily on product thinking: defining clear success metrics, interpreting metric trade-offs, and designing solid experiments. The SQL portion was straightforward but expected clean logic and structured thinking. The case rounds really test how well you connect metrics to real business impact.

      Interview questions [1]

      Question 1

      SQL, success of feature metric evaluation
      Answer question

      Data Scientist, Product Analytics Interview

      15 Jan 2026
      Anonymous interview candidate
      No offer
      Negative experience
      Difficult interview

      Application

      I interviewed at Meta

      Interview

      I got a little caught off guard because I was expecting only 25% coding but it was like 75% SQL coding (5-6 questions related to a business case) followed by a full research design of an A/B experiment. All of this was expected to be complete in 40 minutes so you need to be able to work really fast.

      Interview questions [1]

      Question 1

      One SQL problem required calculating correlation from scratch using SQL, was not expecting that.
      Answer question