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Meta·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Jul 2023Remote

Summary

Meta data scientist interview with a SQL and product analytics question centered on ad performance. The setup was two tables, impressions and clicks/conversions, and you had to design a KPI and write the query yourself rather than just answer a predefined one. Pretty open-ended for a technical screen.

Questions Asked (1)

Q1

Given two tables tracking ad impressions and clicks/conversions, propose a single metric that captures both click-through rate and conversion quality, then write a SQL query that joins the tables and returns the metric broken down by ad_id for the last 7 days.

Product Analytics & MetricsData Modeling
Author's notes

The open-ended part tripped me up more than the SQL itself.

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AI HintsAI Generated

Suggested Approach

Start by defining a composite metric that combines click-through rate (CTR) and conversion rate (CVR) into a single value, such as CTR * CVR (which equals conversions per impression). Then, write a SQL query that joins the impressions and clicks/conversions tables on ad_id and date, filters for the last 7 days, and aggregates the metric by ad_id. Ensure the query handles potential data issues like missing clicks or multiple conversions per impression.

Pro tip: When proposing a composite metric, explicitly state its business interpretation (e.g., conversions per impression) and discuss trade-offs versus using separate metrics. In the SQL, use a LEFT JOIN from impressions to clicks to avoid dropping ads with zero clicks, and consider using COALESCE to handle NULLs.

1. Define the composite metric

Propose a metric that captures both CTR and conversion quality, such as CTR * CVR (conversions per impression) or a weighted score. Explain why it's meaningful for ad performance.

2. Identify table schemas and join keys

Assume tables: impressions (ad_id, date, impressions) and clicks_conversions (ad_id, date, clicks, conversions). Join on ad_id and date to align daily data.

3. Write SQL with aggregation and filtering

Use a LEFT JOIN from impressions to clicks_conversions, filter for last 7 days using date >= CURRENT_DATE - INTERVAL '7 days', and compute the metric as SUM(conversions)/SUM(impressions) grouped by ad_id.

4. Handle edge cases and validate

Use COALESCE to treat NULL clicks/conversions as 0, and consider if multiple rows per ad_id per day exist. Validate that the metric is between 0 and 1 (if using conversions per impression).

Key Points to Mention

  • Definition of CTR (clicks/impressions) and CVR (conversions/clicks), and how their product simplifies to conversions/impressions.
  • Business interpretation: conversions per impression measures overall ad effectiveness from view to conversion.
  • SQL join type: LEFT JOIN to include ads with impressions but no clicks/conversions.
  • Date filtering: use a dynamic date range (e.g., CURRENT_DATE - INTERVAL '7 days') to get last 7 days.
  • Aggregation: SUM(conversions) / SUM(impressions) grouped by ad_id, ensuring correct handling of zeros.
  • Potential data quality issues: duplicate rows, missing dates, or multiple conversions per click.

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