"Tell me about a time you influenced a product decision at Meta or in a Meta-like environment using data. Pick a specific example where the decision was not obvious up front — for example, a launch or rollback decision on Instagram Reels or Facebook Groups, a trade-off across the AARRR funnel, or a debate about whether an experiment result was real versus driven by novelty effect, SRM, or network effects like k-factor. Walk me through the situation, the stakeholders, the analysis you did, how you communicated it, and what decision changed because of your work."
This question tests whether you can do more than produce analysis — it evaluates whether you can shape product direction without formal authority. For a Product Growth Analyst at Meta, that means framing ambiguous growth questions, choosing the right metrics such as retention, IG Save, or viral coefficient, validating experiment quality with checks like CUPED and sample ratio mismatch, and persuading PMs, engineers, and data science partners to act on the evidence.
A strong answer is specific: name the product surface, the dataset or experiment, the conflicting viewpoints, and the business stakes. The best responses show clear judgment, data-driven communication, and ownership — including how you handled pushback, what changed, and what you learned from the outcome.