Data Scientist applicants have rated the interview process at Meta with 3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 100% positive. To compare, the company-average is 59% positive. This is according to Glassdoor user ratings.
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I applied through a recruiter. The process took 2 weeks. I interviewed at Meta (Londres, Angleterre) in Nov 2014
Interview
First quick skype call, with general analytics related questions + little sql exercise.
Second round in person. A round of five separate interviews at Facebook HQ in Euston, each with a different flavour, some more general analytics/BI, some more technical (e.g. coding exercise, sql etc).
Some interviewers were very approachable and easy to talk to, some others much less. There was one case when I honestly thought the person interviewing me had something against me or was upset for some reason, as their behaviour was very rude and confrontational. I am a very peaceful person usually so I let it go.
Probably Facebook asked some random employees to come over from their HQ in the US to do these interviews in change of a little paid vacation in London. I suppose some of them were affected by the jet lag more than others.
Overall it was a good experience; it looked like the day was very well organised.
Interview questions [1]
Question 1
Imagine we see a lot of users filling up a form but not submitting it, why would it be the case and how would you use data to finding it out?
I applied through an employee referral. The process took 1 day. I interviewed at Meta (Vancouver, BC) in Dec 2016
Interview
First a brief introduction of yourself. Then why you choose facebook. The following is an online coding question using mysql. Then question about like/dislike system in the facebook. How to choose emotions and any problems in the A/B test. This question is quite hard and I didn't get it until the interviewer gave a lot of hints.
Interview questions [1]
Question 1
How to choose emotions in like/dislike systems and any problems in the A/B test.
I applied online. The process took 6 weeks. I interviewed at Meta (Menlo Park, CA) in Feb 2017
Interview
I applied online in late December and then spoke to a recruiter for about 15 minutes in early January. I was scheduled for an in-person interview in mid January where I interviewed with one data scientist in a 1:1 interview for an hour for the first round. Another recruiter then called me for a 30 minute prep for the 2nd round. The final, 2nd round interview was about 5 weeks later in late February, with 6 data scientists over 4 hours in the afternoon. I got an offer but it was for a lot less than I was expecting, and we couldn't bridge the gap enough for it to be worth it.
Everyone seemed relatively nice, although I could tell that a lot of the questions are really designed to trip you up, like they want you to miss some detail or edge case. My advice would be to pay attention to every little bit of minutiae regarding the question, make sure you're staying on task, write on the whiteboard, and explain your thoughts. Industry word is that data science at Facebook is not what it once was and is more of a product data analyst role now, so make sure you're really into Facebook products because that's what you'll be analyzing.
Also, I didn't get a single question about dice, cards, or any other brain teaser type questions. All these mentions of NDAs are missing the point of Glassdoor, people can be a little more verbose than "various questions". You can't trademark an interview question or claim it's a trade secret.
Interview questions [9]
Question 1
How would you measure the health of Mentions, Facebook's app for celebrities? How can FB determine if it's worth it to keep using it?
If a celebrity starts to use Mentions and begins interacting with their fans more, what part of the increase can be attributed to a celebrity using Mentions, and what part is just a celebrity wanting to get more involved in fan engagement?
There is a table that tracks every time a user turns a feature on or off, with columns user_id, action ("on" or "off), date, and time.
How many users turned the feature on today?
How many users have ever turned the feature on?
In a table that tracks the status of every user every day, how would you add today's data to it?
We have two options for serving ads within Newsfeed:
1 - out of every 25 stories, one will be an ad
2 - every story has a 4% chance of being an ad
For each option, what is the expected number of ads shown in 100 news stories?
If we go with option 2, what is the chance a user will be shown only a single ad in 100 stories? What about no ads at all?