I applied through an employee referral. The process took 4 weeks. I interviewed at Meta (Schaumburg, IL) in Sep 2023
Interview
Very smooth and streamlined. You are evaluated on attributes like programming skills (you choose a programming language to be tested on), data science math (probabilities and statistics), data science analysis (how you approach a problem, how you come up with hypotheses to test, how you test the hypotheses, etc.) and behavioral (how you handle stress, uncertainty, changes in requirements, etc.). No waiting around for specific people to become available and going through interviews one by one with long gaps in between. All 4 or my interviews took place on the same day just like in the old days when you would have been invited to an office, evaluated by a bunch of people, and sent home at the end of it. The decision came about a week after the interviews. The entire process was less than a month long.
Interview questions [1]
Question 1
SQL: How to join tables with common fields, how to calculate sums and averages, etc. Quite basic, nothing advanced such as window functions or DDL. Data Science math: Calculate expected values, calculate probabilities of simultaneous occurrence of independent events, etc. Pretty basic, nothing advanced like weird distributions or hard to remember facts. Data Science Analysis: Come up with plausible metrics to evaluate a recommendation system. How would you test the recommendation system for bias? What hypotheses can you come up with for detecting bias, and how would you test them? How do you test whether a new system is a net positive or it is cannibalizing an existing system. Behavioral: Tell me about some work you are proud of. How would you handle ambiguity - how do you make recommendations in the face of ambiguity? What do you like about work, what do you dislike?
Tough interview overall—definitely not what I expected. The technical rounds were intense, particularly when they had me design an A/B test for the News Feed ranking algorithm. I had to discuss metrics and sample sizes in detail. Lucky for me, the time I spent on PracHub right before the interview helped me nail that deep-dive question as it mirrored what I practiced. The behavioral questions felt standard but were still challenging. After a whirlwind process, they extended an offer, which I happily accepted.
Interview questions [1]
Question 1
Design an A/B test to evaluate a new ranking algorithm for the Facebook News Feed. Walk through metric selection (engagement, time-spent, MSI, well-being), unit of randomization given network effects between friends, sample size and power calculations, how you'd detect novelty effects vs. true lift, and how you'd handle a guardrail metric regressing while the primary metric is up.
Total 7 rounds: first round for resume screening, second for technical screening, then for on-site virtual with 4 interviews back to back, then hiring manager round after team matching and then salary negotiation with HR
Interview questions [1]
Question 1
Meta’s evaluation rubrics focus heavily on "Product Thinking over Fancy Math". Interviewers want to see if you can operate like a product owner with an analytical mindset, navigating messy scenarios affecting billions of users
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.
