Build dashboard; diagnose engagement–purchase gap

Quick Overview

This question evaluates a data scientist's competency in product analytics and experimentation, focusing on dashboard specification, precise event- and user-level metric definitions, core dimensions, data freshness SLAs, and instrumentation within the Analytics & Experimentation domain.

Build dashboard; diagnose engagement–purchase gap

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Onsite

Build a comprehensive dashboard for the Shopping tab (organic only). Specify primary metrics (e.g., GMV, purchases, unique buyers, PDP CTR) and secondary metrics/guardrails (e.g., bounce rate, search exits, creator engagement). Define each metric precisely (event- and user-level), dimensions (country, device, acquisition channel), and freshness/SLAs. Then, suppose engagement with the tab is high but purchase rate is low: propose a diagnosis plan including segmentation (new vs. returning, search vs. feed, product category), funnel drop-off analysis, instrumentation checks, and experiments you’d run to isolate UX vs. supply issues.

Quick Answer: This question evaluates a data scientist's competency in product analytics and experimentation, focusing on dashboard specification, precise event- and user-level metric definitions, core dimensions, data freshness SLAs, and instrumentation within the Analytics & Experimentation domain.

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Oct 13, 2025, 9:49 PM
hardData ScientistOnsiteAnalytics & Experimentation
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Build a Comprehensive Dashboard for the Shopping Tab (Organic Only)

Context

Assume the Shopping tab is an in-app surface for organic product discovery and purchase. Exclude paid ads and sponsored placements from all metrics. Purchases may occur via in-app checkout or via offsite merchant redirect with server/pixel attribution.

Task

  1. Specify a comprehensive dashboard for the Shopping tab (organic only):
    • Primary metrics (e.g., GMV, purchases, unique buyers, PDP CTR).
    • Secondary metrics and guardrails (e.g., bounce rate, search exits, creator engagement).
    • Define each metric precisely at both the event-level and user-level where relevant.
    • List core dimensions (e.g., country, device, acquisition channel).
    • Provide data freshness and SLAs.
  2. Suppose engagement with the tab is high but purchase rate is low. Propose a diagnosis plan that includes:
    • Segmentation (new vs. returning, search vs. feed entry, product category).
    • Funnel drop-off analysis.
    • Instrumentation checks.
    • Experiments you would run to isolate UX vs. supply (inventory/price/trust) issues.
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