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Meta·Data Scientist·Technical Phone Screen·Senior

Senior
Apr 2026

Summary

A product-meets-data-science question at Meta where they basically asked me to design a full recommendation feature from scratch, including the algorithm, the UX, and how to measure it. Pretty broad scope for one question.

Questions Asked (1)

Q1

A food delivery app wants to add a 'Restaurants You May Know' section to the home page. Why would users and the business want this, how would you design the recommendation algorithm and the user experience end to end, and how would you measure whether it actually worked?

Product Sense & IdeationA/B Testing & ExperimentationProduct Analytics & Metrics
Author's notes

This felt like three questions crammed into one and I kind of froze deciding where to start.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the product goal and user segments, then articulate the value proposition for both users and the business. Next, outline the algorithm design, including data sources, features, and model choice, and describe the end-to-end UX. Finally, define success metrics and an A/B testing plan to measure impact.

Pro tip: Tie every design choice back to a measurable hypothesis and consider potential biases like popularity bias or feedback loops. Show that you can balance short-term engagement with long-term user satisfaction.

1. Clarify Goals and Users

Ask clarifying questions to understand the product's objectives, target users, and constraints. Identify user segments and their needs.

2. Value Proposition

Explain why users would want this feature (e.g., discovery, convenience) and how it benefits the business (e.g., increased orders, retention).

3. Algorithm Design

Describe data sources (user behavior, restaurant attributes), features, model choice (e.g., collaborative filtering, content-based), and how to handle cold start and scalability.

4. User Experience

Outline the end-to-end UX: placement on home page, presentation (e.g., carousel), explanations, and feedback mechanisms.

5. Measurement and Experimentation

Define success metrics (e.g., CTR, conversion, retention), design an A/B test, and consider guardrail metrics and long-term effects.

Key Points to Mention

  • User segmentation and personalization
  • Cold start problem and how to address it
  • Offline evaluation metrics (e.g., precision@k, recall)
  • Online A/B testing with guardrail metrics
  • Business metrics like order frequency and retention
  • Ethical considerations and bias mitigation

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