I applied through other source. I interviewed at Meta (Londres, Inglaterra) in Jul 2019
Interview
The first stage is a screening call with a recruiter, then there was an online interview data scientist comprising of two parts - analytical and technical. The analytical part was a case study about a specific new feature and I was asked to give possible metrics and evaluate the feature's performance. The technical part was a SQL type question (which could have been answered also in Python or R). The third part would have been a series of on-site interviews, but I didn't pass the second stage.
Interview questions [1]
Question 1
I was given two tables of friends and interactions on a plain text environment and asked several questions that required joining, grouping, aggregating and doing some other manipulations on the data to get the answer
Conversation with recruiter in email. Technical screening round where they ask about SQL and product sense. Onsite-Loop with four rounds. They ask about SQL, Product Sense, Statistics, Behavioural questions. The difficulty is average.
The technical round kicked off with a design question about A/B testing for Facebook Reels, which I found engaging. Then, I tackled a SQL query on user comments and how to account for novelty effects in ongoing experiments. Thankfully, I had prepared with the company-specific questions on PracHub, and it made a real difference in my confidence. The entire process felt smooth, and after some behavioral questions, I received an offer that I happily accepted.
Interview questions [3]
Question 1
Design an A/B test for a Facebook Reels ranking change and describe how you would interpret the results
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