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Meta·Data Scientist·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Meta DS interview for what looked like a product analytics role. One big open-ended question about a notification feature on a C2C marketplace, covering everything from pre-launch framing to post-ship experiment design. Pretty exhausting to get through in one sitting.

Questions Asked (2)

Q1

You're a DS on a C2C marketplace app. The team wants to add a feature where buyers can opt into notifications for similar listings. Before building anything, how would you decide if this is even worth pursuing? Walk through the user problem, what metrics you'd expect to move, the risks involved, any pre-build data analysis you'd do, and how you'd structure a phased rollout.

Product Analytics & MetricsProduct Sense & IdeationProduct Strategy
Author's notes

This part went okay but I spent way too long on the metrics taxonomy and not enough on the pre-build validation angle.

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AI HintsAI Generated

Suggested Approach

Start by clearly defining the user problem and the hypothesis for how notifications for similar listings would solve it. Then outline the metrics you'd expect to move, the risks, the pre-build analysis you'd conduct, and a phased rollout plan with clear success criteria. Emphasize a data-driven, iterative approach that validates the feature's value before full investment.

Pro tip: Frame your answer around the scientific method: state your hypothesis, define measurable outcomes, and describe how you'd test and learn quickly. This shows you think like an owner, not just an analyst.

1. Define the user problem and hypothesis

Articulate the pain point: buyers miss out on relevant listings because they don't know when similar items are posted. Form a hypothesis: opt-in notifications will increase buyer engagement and conversion by keeping them informed.

2. Identify metrics and expected impact

Select primary metrics (e.g., notification opt-in rate, click-through rate, listing views, purchase conversion) and guardrail metrics (e.g., notification fatigue, unsubscribe rate, app uninstalls). Estimate potential lift based on analogous features or small-scale tests.

3. Assess risks and pre-build analysis

Evaluate risks: spamming users, privacy concerns, technical complexity, and cannibalization of other engagement channels. Conduct pre-build analysis: user surveys, analysis of existing search/browse behavior, and a holdback experiment to gauge interest and potential impact.

4. Design a phased rollout

Propose a phased approach: start with a small A/B test to measure opt-in and engagement, then expand to a larger percentage if metrics are positive. Include clear go/no-go criteria and a plan to iterate based on feedback.

Key Points to Mention

  • User segmentation: identify which buyers would benefit most (e.g., frequent browsers, high-intent shoppers).
  • Notification fatigue: ensure opt-in is truly optional and frequency is controlled to avoid negative user experience.
  • Success metrics: define both primary (e.g., conversion) and secondary (e.g., engagement) metrics, plus guardrails.
  • Pre-build data analysis: leverage existing data to estimate demand and potential impact before building.
  • Phased rollout: start small, measure, learn, and scale; include a holdback group to measure incremental lift.
  • Competitive analysis: see how similar features perform on other marketplaces or platforms.

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

Q2

Assume the feature has been shipped and can be enabled for a subset of users. How do you design the experiment to measure its impact? Be specific about randomization unit, control vs treatment, duration, your primary and guardrail metrics, and how you'd handle issues like opt-in bias, interference, and attribution.

A/B Testing & ExperimentationProduct Analytics & MetricsTechnical Trade-offs
Author's notes

The opt-in selection bias piece is where I think I actually did well.

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AI HintsAI Generated

Suggested Approach

Start by framing the experiment as a randomized controlled trial with the user as the randomization unit, ensuring balanced groups. Then detail the primary metric tied to the feature's goal, guardrail metrics to monitor for regressions, and a duration that captures novelty effects and weekly seasonality. Finally, address practical challenges like opt-in bias, interference, and attribution by proposing mitigation strategies such as intent-to-treat analysis, cluster randomization, and holdout groups.

Pro tip: Emphasize that you would pre-register the experiment design and metrics to avoid p-hacking, and consider using a switchback or cluster randomization if interference is a concern. Also, mention that you'd run a power analysis to determine sample size and duration upfront.

1. Define the experiment design

Specify the randomization unit (e.g., user), control vs treatment groups, and how users are assigned. Ensure the feature is enabled only for the treatment group.

2. Select metrics and duration

Choose a primary metric that directly measures the feature's impact, along with guardrail metrics to detect negative side effects. Determine duration based on power analysis and business cycles.

3. Address bias and interference

Mitigate opt-in bias by using intent-to-treat analysis or by randomizing at a level where opt-in is not a factor. For interference, consider cluster randomization or switchback designs.

4. Handle attribution and analysis

Define attribution windows and methods (e.g., last-touch, multi-touch) and ensure they align with the feature's expected impact. Use appropriate statistical tests and control for covariates.

5. Monitor and iterate

Set up dashboards to monitor metrics in real-time, check for sample ratio mismatch, and be prepared to stop early if guardrails are breached. After the experiment, analyze results and decide on next steps.

Key Points to Mention

  • Randomization unit: typically user-level, but consider session or device if interference is likely.
  • Control vs treatment: ensure both groups are comparable; use holdout groups if needed.
  • Primary metric: tied to feature's goal (e.g., engagement, revenue); guardrail metrics: latency, error rates, user satisfaction.
  • Duration: at least one full week to capture weekly seasonality; use power analysis to determine sample size.
  • Opt-in bias: if feature requires opt-in, analyze by intent-to-treat or use instrumental variables.
  • Interference: if users can affect each other, use cluster randomization (e.g., by geography or social network).
  • Attribution: define clear attribution windows and methods; consider using a holdout to measure incremental lift.

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