Candidates applying for Data Science roles take an average of 60 days to get hired, when considering 1 user submitted interviews for this role. To compare, the hiring process at Meta overall takes an average of 32 days.
Common stages of the interview process at Meta as a Data Science according to 1 Glassdoor interviews include:
One on one interview: 33%
Phone interview: 33%
Presentation: 33%
Here are the most commonly searched roles for interview reports -
Phone interview
Phone Case Study
Sql Interview
Case Study
Case Study
Seemed okay, also chatted with a data scientist for lunch. Stumbled a bit in sql but answered the question. The two case studies were very similar and got hung up on one a bit
Interview questions [1]
Question 1
Let's say you work at yelp. How would you make the recommendation system for restaurants better for top 10?
It went smooth but quite long. I had SQL and ML-related questions. I wish I had known about what kind of questions they would ask beforehand. I would watch mock interviews on Youtube and grab a better sense of it.
Interview questions [1]
Question 1
Q. How would yhou approach high-dimensional dataset
I applied through a recruiter. The process took 2 months. I interviewed at Meta in Nov 2020
Interview
Facebook has a long process with first recruiter screener, then phone screen, then on site with something like 6 rounds, including 3 behavioral interviews, 3 technical ones, one on data manipulation/programming, one on stats/ML and one on problem solving. In data exercises, they might provide you with raw data that needs quite a bit of cleaning, you really need to be prepared to clean character strings on the fly, which unless one already does regularly is essentially a non-starter.
Interview questions [1]
Question 1
There were many, one recurring one on the problem solving side:
- you have databases and users who access them and everyone has access to everything, need to come up with an access model that will require some access permission while not introducing too much friction
- on the stats and modeling side: Rotten tomatoes data, goal is to explore features that predict whether movie will be certified fresh or rotten by critics, data quite raw to turn into a model and iterate in 60 or 90 minutes, whatever it was