I applied through a recruiter. The process took 2 weeks. I interviewed at Meta (San Jose, CA) in Feb 2019
Interview
fb recruiter contacted me for data science position. The interview process involves one or two phone interviews followed by an onsite interview .
Interview questions [1]
Question 1
1- 3 to 4 SQL question from the employee department with join, and conditional selecting
2- Stat question(OLS, assumptions, t-test, F-test in OLS? Are they both necessary or redunt? what metrics can you use, R squared and probability, what are the problems with R2? How does maximum likelihood relate to ols? Under what condition they are the same? If it possible by adding a feature r2 to decreased?
3- Machine learning: logistic regression vs linear regression, what is decision tree, how does it select feature? How does it decide to which feature to select? random forest what is the 2 element of randomness? What is the kernel trick in SVM?
4- Given a list and a target value, how to find the 2 element that the sum of them is equal to target? Space and time complexity? How to improve it? How the dictionary works?
The Interview Process is very structured -
First Tech Screening round - 45 mins (usually can extend a bit depending on the interviewer)
- 2 SQL Questions ( Medium to Hard ) - based on Joins
Full Loop - 4 rounds 45 mins each.
- SQL
- Behavioral
- Analytical Execution - stats & prob, A/B testing, case study
- Analytical Reasoning - Case study
Interview questions [1]
Question 1
Questions on Bayes Theorem, Probability distribution, etc.
I applied online. The process took 6 months. I interviewed at Meta
Interview
Completed 3 rounds of the process, which includes the initial recruiter screen, technical, full loop, and team matching.
Couldn't move past the full loop interview. The interview was very engaging, and I actually enjoyed working through the cases. No crazy questions.
It's all organized. Be prepared to showcase your depth of thinking. Two analytical rounds will make you think on your ability to solve probability and experimentation problems. Have a structure for everything
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