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Diagnose a 10% DAU drop

Last updated: May 26, 2026

Quick Overview

This question evaluates a data scientist's competency in diagnosing a sudden DAU (daily active users) drop, testing skills in metric validation, data quality and ETL checks, segmentation and attribution analysis, causal inference, and monitoring design.

  • hard
  • Yahoo
  • Analytics & Experimentation
  • Data Scientist

Diagnose a 10% DAU drop

Company: Yahoo

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Technical Screen

Yahoo Mail’s DAU on 2025-09-01 is down 10% vs the 7-day baseline (2025-08-25..2025-08-31). You are the on-call product analyst. Outline an end-to-end plan to: (1) verify the drop (metric definition, log health, bot filters, experiment/treatment exclusions, platform/app-version splits); (2) isolate internal vs external causes using a metric tree (DAU = new + returning; returning = retained + resurrected) and segmentations (geo, device, app version, notification eligibility, spam folder rate, inbox load latency, login failures, SMTP delivery latency, outages, release rollouts, paywall/ads); (3) run quick SQL/Python checks to find the top three contributing segments by absolute DAU loss and estimate their contribution; (4) propose a causal test or quasi-experiment (e.g., rollback A/B, holdout by app version, diff-in-diff with synthetic controls using unaffected geos) to validate the suspected cause; (5) recommend immediate mitigations and a monitoring plan with alert thresholds, guardrail metrics (open rate, send success, latency), and a rollback criterion. Be specific about data you’d pull, success metrics, and the order of operations within the first 2 hours.

Quick Answer: This question evaluates a data scientist's competency in diagnosing a sudden DAU (daily active users) drop, testing skills in metric validation, data quality and ETL checks, segmentation and attribution analysis, causal inference, and monitoring design.

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  • Investigate Yahoo Mail's 10% DAU Decline Causes - Yahoo (medium)
Yahoo logo
Yahoo
Oct 13, 2025, 9:49 PM
Data Scientist
Technical Screen
Analytics & Experimentation
4
0

On-Call Incident: Yahoo Mail DAU Down 10% on 2025-09-01

Assume all times are UTC, the product is global, and DAU is the canonical daily active user metric for Yahoo Mail. The 7-day baseline is 2025-08-25..2025-08-31. 2025-09-01 is a Monday and a US holiday (Labor Day), which may or may not be material.

Task

Outline an end-to-end plan to address the incident:

  1. Verify the drop
    • Clarify DAU metric definition and identity, ensure bot/employee filters, and confirm experiment/treatment exclusions.
    • Check log/ETL health and data freshness.
    • Split by platform and app version (web, iOS, Android; version buckets).
  2. Isolate internal vs external causes
    • Use a metric tree: DAU = new + returning; returning = retained + resurrected.
    • Segment by: geo, device, app version, notification eligibility, spam-folder rate, inbox-load latency, login failures, SMTP delivery latency, outages, release rollouts, paywall/ads.
  3. Run quick SQL/Python checks
    • Find the top three contributing segments by absolute DAU loss vs the 7-day baseline.
    • Estimate each segment’s contribution to the total loss.
  4. Propose a causal validation
    • Design a causal test or quasi-experiment (e.g., rollback A/B, holdout by app version, difference-in-differences with synthetic controls using unaffected geos) to validate the suspected cause.
  5. Recommend mitigations and monitoring
    • Immediate mitigations.
    • Monitoring plan with alert thresholds, guardrail metrics (open rate, send success, latency), and a rollback criterion.
    • Be specific about data to pull, success metrics, and the order of operations within the first 2 hours.

Solution

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