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Historical candidate account: this article preserves one person’s reported experience. The interview date was not recorded in the original text, and updating this page does not make the account a current hiring process. Names, levels, durations, questions, outcomes, and approximate compensation are as reported, not independently verified or endorsed by the employer. Confirm the current role and assessment with the recruiter.
The candidate described an analytics-focused product data science interview at Facebook, the company name used in the original account.
In the original account, we spoke with one of our members Jennie, who received a great job offer from Facebook as a product data scientist (analytics track).
Even though she didn't accept the offer, she has a few other offers and decided to join a startup.
She enjoyed the overall interviewing process. However, after the final round of interviews, she had to wait about two weeks before getting the final decision.
Jennie was grateful that the recruiter kept in touch at least every week and kept her in the loop.
The main improvement she wanted was a shorter wait for the final decision.
She shared her overall interview experience, and we hope it will be helpful for anyone interested in Facebook.
TLDR
Candidate: Jennie
How it gets started: recruiter contacted through LinkedIn.
Job level: E4
Year of Experience: 3-5
Degree: M.S. in Statistics
Offer: Yes
TC: ~200K USD
Location: Menlo Park, CA
Interview process: 2 months
Preparation: 4 months
Has a job: yes
Decide to join: No
Timeline
First round: 30 minutes informational call with a recruiter, talked about current work, and asked to schedule a technical interview with a team member.
Second round: half SQL, half product sense.
SQL question: friend acceptance rate (similar to sqlpad question #91).
Product sense: should we add a LOVE button on Facebook? Why?
Third round: Product sense (based on the feedback from the last round)
How to decide if two friends are the 'BEST' friends?
Final round: (4 interviewers)
- Analysis: Product. KPIs to evaluate a new feature/product
- Analysis: Applied data. Engagement dropped 15 percent. What would you do?
- Quantitative analysis: probability, prior/post probability, a/b testing
- Technical analysis: SQL
Received the offer two weeks after the final round.
From a reported prompt to a measurable product question
For a proposed LOVE button, clarify what user need differs from the existing reaction options. Define adoption among eligible viewers, meaningful interactions, and guardrails such as unwanted reactions or confusion. More button presses alone do not establish value. For friend acceptance rate, define the request cohort and acceptance window, deduplicate requests, and decide how to treat requests that have not had enough time to be accepted. Do not infer private relationships from covert or sensitive data when discussing the reported “best friends” prompt.
Practice one reported prompt, state the data and assumptions, produce a checkable answer, and explain one limitation. Then change a condition and see whether your reasoning still holds. Use this related practice resource for a follow-up; it is separate from the historical interview report.
