Meta·Data Scientist·Technical Phone Screen
May 2026
Meta Data Scientist interview with a heavy stats and probability focus. The core problem was a multi-part fake account detection question that went pretty deep into Bayes, binomial distributions, and then got messier when they introduced signal correlation at the end.
- You have a fake account classifier that uses five independent signals. Each signal fires with probability 0.8 for fake accounts and 0.05 for real ones. For a threshold of k=2 (flag if at least 2 signals fire), compute the probability of flagging a fake account and the probability of flagging a real account using the binomial distribution.
- Given a base rate of 1.5% fake accounts and 5 million accounts scanned per day, use Bayes' theorem to find P(fake | flagged) and the expected number of flagged accounts. Is an 80,000-per-day manual review queue sufficient?
- Find the smallest threshold k such that the expected daily flagged volume stays within 80,000 plus or minus 5%, while also maximizing P(fake | flagged). Walk through the trade-off between precision and recall.
- If the five signals are not actually independent and have a pairwise correlation of 0.2 within each class, how does that change your analysis and what assumptions break down?
“Binomial CDF complement, nothing exotic.” The rest of the author's notes on Data Scientist interview at Meta, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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