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Meta·Data Scientist·Technical Phone Screen·Senior

Senior
Jul 2023Remote

Summary

Meta DS interview with a SQL-heavy visibility metrics question. The setup was straightforward but the real test was whether you'd define your metric before writing a single line of code.

Questions Asked (2)

Q1

Given a shop events table with view and purchase events, write SQL to compute each shop's daily visibility rate, defined as total views divided by unique visiting users.

Product Analytics & MetricsData Modeling
Author's notes

I jumped straight into writing the query and skipped defining what 'visibility rate' actually meant.

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AI HintsAI Generated

Suggested Approach

First, clarify the table schema and the definition of 'unique visiting users' (e.g., distinct users with any event or only view events). Then, write a SQL query that aggregates views and distinct users per shop per day, and compute the ratio. Use a subquery or CTE to separate aggregation from division.

Pro tip: Mention that you would validate the metric by checking edge cases like days with zero unique users, and discuss whether to include purchase events in the denominator. This shows attention to data quality and metric definition.

1. Clarify requirements and schema

Ask about the table columns (e.g., shop_id, event_type, user_id, timestamp) and confirm the definition of 'unique visiting users' (e.g., distinct users who triggered any event or only view events).

2. Aggregate views and users per shop per day

Use a GROUP BY on shop_id and date to count view events and distinct user_ids. Ensure you filter for view events when counting views, but consider all events for unique users if that's the definition.

3. Compute the visibility rate

Divide total views by unique users, handling division by zero (e.g., using NULLIF or CASE). Round or format as needed.

4. Write the final SQL query

Combine steps into a single query, using CTEs or subqueries for clarity. Optionally, include a date filter for a specific period.

Key Points to Mention

  • Definition of 'unique visiting users' and whether it includes all event types or only views.
  • Handling of days with zero unique users to avoid division by zero errors.
  • Use of DATE() or equivalent function to extract the day from a timestamp.
  • Efficiency considerations: using COUNT(DISTINCT) and appropriate indexing.
  • Potential need to filter out test shops or invalid events.
  • Interpretation of the metric: views per user as a measure of engagement or visibility.

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

Q2

Before writing any code, propose a clear definition for a shop visibility metric and confirm it with the interviewer.

Product Analytics & MetricsAdaptability & Ambiguity
Author's notes

This is basically a communication test disguised as a SQL question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the business goal behind the metric—what does 'shop visibility' mean for Meta's products and users? Propose a concrete definition with a formula, then walk through edge cases and validation before confirming with the interviewer.

Pro tip: Frame your definition as a hypothesis and explicitly invite the interviewer to challenge it—this shows you value alignment over being right and mirrors how successful data scientists operate in ambiguous settings.

1. Clarify the business context

Ask the interviewer what problem the metric should solve and which stakeholders will use it. This ensures your definition aligns with Meta's goals and avoids solving the wrong problem.

2. Propose a concrete definition

State a clear, measurable definition, such as 'the percentage of active users who view at least one shop product in a session.' Include the numerator, denominator, and time window.

3. Justify with rationale and examples

Explain why this definition captures 'visibility' and give examples of what would and wouldn't count. Connect it to potential business outcomes like engagement or revenue.

4. Address edge cases and trade-offs

Discuss ambiguous cases (e.g., repeated views, bot traffic, different surfaces) and how you'd handle them. Mention alternative definitions and their pros/cons.

5. Confirm and iterate

Explicitly ask the interviewer if this definition meets their expectations or if they'd adjust it. Show openness to refining based on feedback.

Key Points to Mention

  • Define the metric with a clear numerator and denominator (e.g., users who view a shop / total active users).
  • Specify the time window (daily, weekly) and unit of analysis (user, session).
  • Consider different surfaces where shops appear (Feed, Stories, Reels) and whether to aggregate or separate them.
  • Discuss how to handle edge cases like accidental views, bot traffic, or repeated views.
  • Tie the metric to business impact (e.g., increased engagement, ad revenue, or user retention).
  • Mention validation: how you'd test the metric's stability and correlation with business outcomes.

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