I applied through an employee referral. The process took 3 weeks. I interviewed at Meta in May 2016
Interview
Interview process was good. The recruiter was very helpful and interviewers were very friendly. The onsite interviews were back to back and felt a little rushed at times as they were only 30 minutes each. When preparing for an onsite interview day mentally prepare for a rapid pace!
Interview questions [1]
Question 1
What is the probability of drawing two cards (from the same deck of cards) that have the same suite?
I applied through an employee referral. The process took 4 weeks. I interviewed at Meta in Mar 2016
Interview
It was a pain to communicate with the recruiter. She seems so busy that does not have time for basics. Took several back-and-forth emails and more than a week to book a 10-min phone screening with her. The email she sent was addressed to another candidate. Despite the available time slots I provided, the technical interview was still scheduled at a time that I wouldn't be available. No biggies after all, but the experience was just frustrating.
Interview questions [1]
Question 1
Find the expected revenue/loss of throwing two dices.
One HR interview, one takehome data challenge, one shared screen with SQL and one onsite with several 1:1 interviews. They check your coding skills and product sense via the takehome, your sql skills via the shared screen interview and machine learning theory as well as product sense during the onsite.
They let you choose the language for the takehome and onsite there is no coding on the board. So you just need to know one language (whichever you want, although I think they prefer R or Python) + SQL. No C++/Java/etc stuff and no CS algo questions.
Interview questions [1]
Question 1
Data challenge was very similar to the ads analysis challenge on the book the collection of data science takehome challenge, so that was easy (if you have done your homework).
SQL was: you have a table where you have date, user_id, song_id and count. It shows at the end of each day how many times in her history a user has listened to a given song. So count is cumulative sum.
You have to update this on a daily basis based on a second table that records in real time when a user listens to a given song. Basically, at the end of each day, you go to this second table and pull a count of each user/song combination and then add this count to the first table that has the lifetime count.
If it is the first time a user has listened to a given song, you won't have this pair in the lifetime table, so you have to create the pair there and then add the count of the last day.
Onsite: lots of ads related and machine learning questions. How to build an ad model, how to test it, describe a model. I didn't do well in some of these.