The interview process can last from 1-3 months.

• Stage 1: Phone screen with recruiter, with basic logic questions (checking if the candidate knows basic probability, dependant and independant stats concepts).
  
• Stage 2: 3 interviews with data scientists using collabedit
  2 technical coding interviews and 1 technical statistical interview. In some cases, the 3 sections were slightly different: product/business sense section where you discuss FB products, how you would improve them, and what type of metrics you would use to gauge success; SQL coding to show basic competency in coding; and a section on statistics.
  
• Stage 3: Performance review
  
• Stage 4: Onsite interview
  The onsite includes a meet and greet with a recruiter, 5 interviews with data scientists, and lunch with a data scientist. 3 of the interviews are technical interview and 2 are business cases.

• Q 1: In Mexico, if you take the mean and the median age, which one will be higher and why?
• Q 2: If you draw 2 cards from a shuffled 52 card deck, what is the probability that you'll have a pair?
• Q 3: Given a table that each day shows who was active in the system and a table that tracks ongoing user status, write a procedure that will take each day's active table and pass it into the ongoing daily tracking table. Possible states are: * user stayed (yesterday yes, today yes) * user churned (yesterday yes, today no) * user revived (yesterday no, today yes) * user new (yesterday null, today yes) Note: you'll want to spot and account for the undefined state.
• Q 4: How can Facebook figure out when users falsify their attended schools?
• Q 5: SQL queries with basic group by self joins and inner queries. The problem could be solved by analytical queries.
• Q 6: SQL and why is the number of likes increasing?
• Q 7: There's a game where you are given two fair six-sided dice and asked to roll. If the sum of the values on the dice equals seven, then you win $21. However, you must pay $5 to play each time you roll both dice. Do you play this game? And in follow-up: What is the probability of making money from this game?
• Q 8: We at Facebook would like to develop a way to estimate the month and day of people's birthdays, regardless of whether people give us that information directly. What methods would you propose, and what data would you use, to help with that task?
• Q 9: Imagine we see a lot of users filling up a form but not submitting it, why would this be the case and how would you use data to figure it out?
• Q 10: Given a list A of objects and another list B which is identical to A except that one element is removed, find that removed element.
• Q 11: 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?
• Q 12: There is a table that tracks every time a user turns a feature on or off, with columns for 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?
• Q 13: If 70% of Facebook users on iOS use Instagram, but only 35% of Facebook users on Android use Instagram, how would you investigate the discrepancy?
• Q 14: How do you measure newsfeed health?
• Q 15: If a PM says that they want to double the number of ads in Newsfeed, how would you figure out if this is a good idea or not?
• Q 16: We have two options for serving ads within Newsfeed: 1) out of every 25 stories, 1 will be an ad, or 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?
• Q 17: How do you map nicknames (Pete, Andy, Nick, Rob, etc) to real names?
• Q 18: Facebook sees that likes are up 10% year after year, why could this be?
• Q 19: How many high schools that people have listed on their profiles are real? How do we find out, and deploy at scale, a way of finding invalid schools?
• Q 20: How would you choose emotions in like/dislike systems and any problems in the A/B test?
• Q 21: What experiment would you run to implement new features on Facebook?
• Q 22: Given a series of tables; write the SQL code you would need to count subpopulations through joins.
• Q 23: What is the expectation of the variance?
• Q 24: How do you determine a product's success and scenarios that involve you to code/query using SQL/R/Python and analyze data sets.
• Q 25: The success ratio of sending messages given sent and receive tables.
• Q 26: Make a histogram of 2 variables.
• Q 27: We have a product that is getting used differently by two different groups. What is your hypothesis about why and how would you go about testing it?
• Q 28: Given a specific product, come up with some potential improvements and design a series of experiments for testing/implementing these changes.

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