Evaluate AI-assisted ad creation
Company: Meta
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Onsite
A company is launching an AI-assisted ad creation feature for advertisers. The tool helps advertisers generate ad copy and creatives, and the team wants to know whether it should be rolled out broadly.
How would you evaluate this feature? Your answer should address all of the following:
- What are the primary success metrics for the feature?
- What guardrail metrics would you track across advertiser outcomes, user experience, auction health, and policy/compliance?
- How would you distinguish short-term efficiency gains from true long-term value?
- How would you design an experiment, including unit of randomization, triggered analysis, and segmentation?
- How would you handle interference, since changes in ad creation can affect auction competition and other advertisers?
- How would you test whether growth in AI-created ads is incremental rather than simply cannibalizing existing creation sources?
- What biases or confounders should you worry about, and what would you do if a clean randomized experiment were not feasible?
Quick Answer: This question evaluates a candidate's competence in product analytics, causal inference, experimentation design, metric definition, and monitoring for AI-enabled advertising features.