Meta Interview Questions — Page 181

Meta·Data Scientist·Technical Phone Screen

Jun 2026

Meta DS interview focused entirely on a product case for a B2C chatbot aimed at retailers. The whole session was one big scenario with several sub-questions layered in, including metrics, experiment design without A/B testing, and chart critique. Dense and a bit exhausting.

What value does a B2C chatbot product provide to retailers and to the platform, and is it worth launching?
What metrics would you track for this chatbot from user, business, and model performance perspectives? Justify your choices.
Since A/B testing isn't possible here, how would you size the opportunity and identify signals that the launch is working?
You're given a visualization related to the chatbot's performance. What are its strengths and weaknesses?

“I started with the retailer side since that felt more concrete: faster response times, lower support costs, maybe better conversion if customers get answers quickly.”

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Meta·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round at Meta for a Data Scientist role, pretty standard team-fit stuff. Three questions, all focused on self-reflection and past experience. Nothing technical, which honestly threw me a bit since I'd been grinding SQL and stats prep.

Tell me about a time you faced a conflict at work. How did you handle it, and what was the outcome?
What is the most useful thing you learned in a previous role that you still apply today?
Describe a project you are most proud of and why.

“I went with a disagreement I had with a PM over how we were defining a success metric for a launch.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026

Meta DS interview with a causal inference question that felt more like a research design problem than anything I'd call typical analytics. The setup was straightforward but the depth they expected was not.

Management believes revenue growth from a particular traffic source is coming at the expense of other sources rather than being genuinely new growth. How would you design an analysis to test whether next year's revenue increase from that source is offset by declines elsewhere?

“This one tripped me up because my first instinct was to just plot the trends side by side and call it a day.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round at Meta for a software engineer role. Four questions, all pretty standard leadership-and-growth territory, but they go deep enough that vague answers won't cut it.

Walk me through a project you're proud of, including your goals, what you specifically owned, any technical or organizational hurdles, and what the measurable impact was.
Tell me about a time you had to work through a lot of ambiguity. How did you figure out what was actually needed, what risks did you flag, and how did you keep iterating?
Give me an example that shows you have a growth mindset. What feedback did you receive, what did you actually do about it, and what changed as a result?
Describe a situation where you had a difficult relationship with a manager or key stakeholder. What did you do to manage it, how did you communicate through it, and what did you take away from it?

“I picked a project I genuinely liked but probably undersold the impact because I couldn't remember the exact numbers on the spot.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026

Meta coding round with an AI-assisted debugging twist. The problem was a flaky test suite and you had to figure out why it failed intermittently, which sounds easy until you're staring at a map and wondering why state is leaking between assertions.

You're given a test suite that fails intermittently. Diagnose the root cause and fix it so the tests pass reliably.
If modifying the tests is off the table, how would you prevent the mutating map access from being possible at all?

“The bug was subtle.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Product strategy question for what seems like a PM role at Meta. Just one question but it's the kind that can spiral in a dozen directions if you're not careful.

Should Facebook build a tablet?

“This one looks deceptively simple and I think that's the point.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026

This was a deep product analytics case for a Data Scientist role at Meta, structured around designing an end-to-end experiment for a Marketplace feature. Seven sub-questions in one prompt, which felt like a lot to juggle in real time.

State your mission for the Verified Seller Badges feature precisely, and write primary and secondary hypotheses that are falsifiable.
Define a North Star Metric and 4 to 6 supporting metrics, including at least two counter or guardrail metrics. Explain why each is diagnostic and how to compute it at user and geo level.
Propose a geo-level clustered A/B test for this feature. Specify the cluster unit, stratification variables, matching strategy, number of clusters per arm, traffic ramp plan, duration, and how you'd handle contamination and spillovers.
How would you estimate the minimum detectable effect for your North Star Metric, including variance assumptions and design effects from clustering?
What specific events and attributes would you need in your logs to compute all the metrics and diagnose the mechanism behind any observed lift?
The test shows a 2% lift on the North Star Metric, a small non-significant drop in sessions per user, and a borderline significant increase in fraud reports. Walk through your launch decision and do a back-of-envelope revenue estimate.
Name a third-party signal you'd use to improve targeting for this feature, and discuss privacy and compliance considerations plus how you'd validate its incremental value without bias.

“I fumbled the 'falsifiable' part a bit.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round at Meta for a SWE role, pretty standard stuff. One question, not much to report beyond the usual.

Tell me about a time you faced a significant challenge and pushed through to hit your goal.

“Classic behavioral question, structured my answer as situation-action-result and it felt fine.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Meta PM interview focused on accessibility product prioritization for VR, specifically a forced-choice between two voice features under real engineering constraints. The problem was layered enough that I kept second-guessing my own framework mid-answer.

You can only ship one feature first due to engineering capacity: voice-to-text or a voice assistant for an accessibility-focused VR product. Walk through how you would decide which to prioritize, including your framework, the factors that matter most, your actual recommendation, and what evidence would flip your answer.
What framework would you use to compare these two features before making a prioritization call?
How do voice-to-text and a voice assistant differ in terms of user value and implementation risk?
What clarifying questions would you ask before committing to a recommendation?
What if user research shows people mostly want better social communication, not help with navigation?
How would you design fallback controls for when speech recognition fails?
What privacy controls should users have over voice features in this product?
How would you measure whether a voice command feature is actually working, beyond just tracking usage?
How would you approach internationalizing a voice feature for a global user base?

“This is the core question and it's a lot.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026

Interviewed for a SWE role at Meta, got asked a pretty foundational question about APIs. Short and straightforward, nothing too wild.

What is an API and how does it work?

“Blanked a little on how to structure this without sounding like I was reading from a textbook.”

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