Meta·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026

Summary

A data science interview at Meta centered on the Facebook Dating product launch. Just one question but it had a lot of surface area, covering mission definition, goal setting, and metrics all in one shot.

Questions Asked (1)

Facebook just launched a dating feature inside its existing app. How would you define the mission of Facebook Dating, and what goals and metrics would you track to measure its success?

Product Analytics & MetricsProduct Sense & IdeationProduct Strategy

Author's notes

This is the kind of question where you can spiral fast if you don't anchor on mission first.

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AI HintsAI Generated

Suggested Approach

Start by defining a clear mission for Facebook Dating that aligns with Meta's broader goal of fostering meaningful connections. Then, outline a hierarchy of goals (e.g., user acquisition, engagement, retention) and corresponding metrics (e.g., matches per user, conversation rate, DAU/MAU) that measure success at each stage. Finally, tie the metrics back to the mission and consider potential trade-offs or ethical implications.

Pro tip: Emphasize that success isn't just about matches but about meaningful relationships formed, and suggest tracking long-term retention of couples as a north-star metric. Also, mention the importance of privacy and safety as foundational to trust in a dating product.

1. Define the Mission

Craft a concise mission statement that captures the purpose of Facebook Dating, such as 'to help people find meaningful relationships within their existing social graph.' Ensure it aligns with Meta's overall mission of bringing people together.

2. Identify Goals

Break down the mission into actionable goals: user acquisition (sign-ups), activation (profile completion, first match), engagement (messages sent, matches per user), retention (weekly active users, repeat usage), and outcome (relationships formed).

3. Select Metrics

For each goal, choose specific, measurable metrics. For example: DAU/MAU for engagement, match rate for activation, conversation rate for engagement, and 30-day retention for retention. Consider a north-star metric like 'number of meaningful conversations per week.'

4. Prioritize and Balance

Prioritize metrics based on the product stage (early-stage focus on acquisition and activation; later-stage on retention and outcomes). Balance growth metrics with quality metrics (e.g., report rate, safety incidents) to avoid harmful trade-offs.

5. Connect to Mission and Iterate

Explain how the metrics ladder up to the mission. Suggest a process for regularly reviewing metrics and iterating on the product to ensure long-term success and alignment with user needs.

Key Points to Mention

North-star metric: e.g., number of meaningful relationships formed or long-term retention of couples.
Funnel metrics: sign-ups, profile completion, matches, conversations, and dates.
Engagement metrics: DAU/MAU, messages per match, time spent.
Retention metrics: 7-day and 30-day retention, churn rate.
Safety and privacy metrics: report rate, block rate, data privacy compliance.
Alignment with Meta's mission and leveraging the existing social graph for better matches.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.