Onsite interviews#
The final stage of the interview is a 2.5 hour series of interviews at either the Menlo Park, Seattle, or the New York Facebook campus. There are 4 different 30-minute interviews that each cover a different case study. There’s also a 30-40 minute lunch break to discuss the role with a current data scientist.
You have the entire 30-minute interview to answer each question:
- 1 SQL technical question
- 1 product interpretation question
- 1 quantitative analysis question
- 1 applied data question
The SQL technical question will be similar in format to the technical screening questions; you’ll receive a data set and be asked to solve problems using SQL. However, this SQL question tends to be more difficult and has a longer solution than those in the technical screening.
The product interpretation question asks you to measure product performance with details like target KPIs and how to implement a/b testing. You might be asked just to walk through this or you may have to create a high-level plan of the implementation.
An example problem for this would be:
“How would you measure the performance of X new feature?”
The quantitative analysis question is a basic statistics problem that tests if you understand the basics of statistical data analysis. Many candidates find this to be the easiest part of the interview as it is simply a baseline that you’ve not forgotten the fundamentals.
Example questions for this are things like:
- What is Bayes’ theorem and when would you use it?
- What is hypothesis testing?
- What is p-value and how do you interpret it in context?
- List assumptions about data in the context of linear regression.
- How would you explain the application of probability to your product manager?
The applied data question asks you to consider a solution at a high-level. You’ll outline your process, list any assumptions you have, describe possible shortcomings and how you’ve prepared for them, and explain how you reached your conclusions. The interviewer will ask follow-up questions during the process to see how deeply you’re thinking about this solution.
Questions for this section are intentionally broad, such as:
- Do people interact more or less on Facebook with their siblings?
- How would you measure interaction?
- How would you determine if people are siblings?
- How could Facebook use this information?
Or
- How does activity vary depending on the season?
What region/s are you looking at?
How would you weight activity, is a comment worth more than a like?
- What factors would you use to distinguish users?
- How could Facebook use this information?
Between two of these interviews, you’ll get a casual 30-40 minute interview with a current data scientist to ask them about their day-to-day responsibilities, challenges, and anything else you’re curious about.
This is essentially a behavioral interview to see if you’ve got the right mindset and excitement to fit the company. Ask them insightful questions that show you’re thinking about the job, and turn your charm up to 11!
Some good questions to ask are:
- What was the most difficult project of your career and how did you solve it?
- What are the unique benefits of being a Facebook data scientist?
- What tips do you wish you had when you started working at Facebook?
- What is your favorite Facebook feature to work on and why do you like it?