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Meta·Data Scientist·Technical Phone Screen·Senior

SeniorPrefer not to say
Apr 2026Remote

Summary

Meta DS interview that was essentially a full research design case study. One big open-ended question about building a research plan for a new product feature, split into two parts. Felt more like a take-home prompt delivered live, which I was not expecting.

Questions Asked (2)

Q1

Before launching a group calling feature, how would you design a survey study to validate user needs? Walk through the specific questions you'd ask, the response formats, how you'd recruit participants, and how you'd connect survey responses to actual behavioral data without compromising privacy.

A/B Testing & ExperimentationProduct Analytics & MetricsAdaptability & Ambiguity
Author's notes

This is where I spent most of my time and honestly undersold the vignette piece.

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AI HintsAI Generated

Suggested Approach

Start by defining the research objective and hypotheses, then design a mixed-methods survey with both quantitative and qualitative questions. Outline recruitment, privacy-preserving data linkage, and analysis plan to connect survey responses with behavioral data.

Pro tip: Emphasize privacy by using aggregated or anonymized behavioral data and obtaining explicit consent for data linkage. Also, pilot the survey to refine questions and ensure clarity.

1. Define Objectives and Hypotheses

Clarify what you want to learn about user needs for group calling, such as frequency of group calls, pain points, and feature preferences. Formulate testable hypotheses to guide the survey design.

2. Design Survey Questions and Response Formats

Develop a mix of closed-ended (e.g., Likert scale, multiple choice) and open-ended questions to capture both quantitative and qualitative insights. Ensure questions are unbiased and directly tied to the objectives.

3. Recruit Participants

Identify the target population (e.g., existing users who engage in group messaging or calls) and recruit via in-app prompts, email, or panels. Consider incentives and ensure diverse representation.

4. Connect to Behavioral Data with Privacy Safeguards

Plan to link survey responses to behavioral data using anonymized user IDs, with explicit consent. Use aggregation or differential privacy techniques to protect individual privacy.

5. Analyze and Validate

Analyze survey results to identify patterns and validate hypotheses. Cross-reference with behavioral data to triangulate findings and assess actual usage versus stated preferences.

Key Points to Mention

  • Use a mix of quantitative and qualitative questions to capture both breadth and depth of user needs.
  • Include questions about current group calling habits, pain points, desired features, and willingness to adopt.
  • Recruit a representative sample of the target user base, possibly using stratified sampling.
  • Ensure privacy by anonymizing data, obtaining consent, and using aggregation or differential privacy when linking to behavioral data.
  • Pilot test the survey to refine questions and reduce bias.
  • Triangulate survey findings with behavioral data to validate self-reported needs against actual usage patterns.

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

Q2

Without access to internal data, how would you estimate the U.S. adoption rate of a competitor's group calling feature to within roughly 20% relative error? What signals would you use, how would you collect them, and how would you triangulate them into a single estimate?

Product Analytics & MetricsProduct StrategyTechnical Trade-offs
Author's notes

I actually liked this one more than I expected.

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AI HintsAI Generated

Suggested Approach

Break the problem into a top-down market sizing (total addressable users × plausible adoption rate) and a bottom-up signal-based estimate (e.g., app downloads, reviews, social mentions), then triangulate by comparing and reconciling the two. Focus on transparent assumptions and sensitivity analysis to bound the estimate within 20% relative error.

Pro tip: Anchor your estimate to a known reference class (e.g., adoption of similar features like Zoom or WhatsApp group calls) and explicitly state your confidence interval—interviewers care more about your reasoning than the exact number.

1. Define the metric and scope

Clarify what 'adoption rate' means: percentage of U.S. smartphone users who have used the competitor's group calling feature at least once in a given period. Specify the denominator (e.g., all U.S. adults, or users of the competitor's app) and the time frame.

2. Identify external signals

List available public signals: app store rankings and download estimates (Sensor Tower, App Annie), user reviews mentioning group calls, social media mentions (Twitter, Reddit), Google Trends for related keywords, and competitor press releases or earnings calls.

3. Collect and quantify signals

For each signal, estimate a proxy metric (e.g., number of reviews mentioning group calls × average review rate to infer usage). Use web scraping, APIs, or public dashboards to gather data, and document assumptions.

4. Triangulate into a single estimate

Combine signals using a weighted average or range-based approach. For example, take the median of estimates from different methods, and calculate a confidence interval. Adjust for known biases (e.g., review bias toward power users).

5. Validate and bound error

Perform sensitivity analysis on key assumptions (e.g., review rate, overlap between signals). Compare with any available benchmarks (e.g., adoption of similar features) to ensure the estimate is within 20% relative error.

Key Points to Mention

  • Top-down vs. bottom-up estimation approaches
  • Use of public data sources like app store analytics, social listening, and search trends
  • Adjusting for biases in user-generated content (e.g., review bias, selection bias)
  • Triangulation methods: weighted average, median of estimates, or Bayesian updating
  • Sensitivity analysis and confidence intervals to bound error
  • Benchmarking against analogous features or products (e.g., Houseparty, Zoom)

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