Meta
Hard
Data ScientistDetermine Success Metrics for New Group Video-Call Feature
Analytics & Experimentation
30
0
July 12, 2025
Root Cause Analysis Interview Questions
Root cause analysis questions test how you investigate unexpected metric movements and diagnose product or data issues.
Expect scenario-based questions like "DAU dropped 10% this week — how would you investigate?"
Interviewers evaluate your structured approach, ability to prioritize hypotheses, and how you communicate findings.
782Questions
76Companies
Easy 97 • Medium 41 • Hard 12Difficulty mix
Common root cause analysis patterns
- Structured investigation framework (confirm → segment → hypothesize → validate)
- Segmentation by platform, geography, user cohort, and device
- Checking data pipeline issues before investigating product changes
- Funnel decomposition to isolate where the drop occurs
- Correlation with external events (holidays, competitor launches, outages)
- Quantifying impact to prioritize investigation
Root cause analysis interview questions
Filter and sort
Gusto
Easy
Data ScientistCompute p-values for 2 variants vs control
Analytics & Experimentation
6
0
November 2, 2025
DoorDash
Hard
Data ScientistDiagnose cold-food spike and design experiments
Analytics & Experimentation
8
0
October 13, 2025
DoorDash
Hard
Data ScientistEvaluate and test a Top Dasher program
Analytics & Experimentation
9
0
October 13, 2025
Meta
Easy
Data ScientistEvaluate AI-assisted ads creation feature
Analytics & Experimentation
7
0
February 15, 2026
OpenAI
Medium
Data ScientistHow would you evaluate a free-trial A/B test?
Analytics & Experimentation
4
0
October 8, 2025
Apple
Medium
Data ScientistInvestigate Conversion Drop: Metrics, Analyses, Techniques Explained
Analytics & Experimentation
22
0
July 12, 2025
LinkedIn
Hard
Data ScientistLinkedIn Product Case Opportunity Sizing
Analytics & Experimentation
10
0
April 30, 2025
OpenAI
Hard
Data ScientistAssess free-month promotion impact
Analytics & Experimentation
5
0
January 22, 2026
Meta
Easy
Data ScientistHow would you compare Facebook vs Instagram Stories?
Analytics & Experimentation
8
0
November 1, 2025
Meta
Easy
Data ScientistHow would you evaluate pixel-issue notifications?
Analytics & Experimentation
7
0
February 18, 2026
DoorDash
Medium
Data ScientistMeasure Impact of Merchant Variety on Consumer Experience
Analytics & Experimentation
17
0
August 4, 2025
Instacart
Easy
Data ScientistDesign a pricing experiment with network effects
Analytics & Experimentation
9
0
February 6, 2026
Uber
Hard
Data ScientistImprove Estimated Time of Arrival for Uber Riders
Analytics & Experimentation
5
0
August 4, 2025
Meta
Medium
Data ScientistDesign "Restaurants You May Know" Recommendation Algorithm
Analytics & Experimentation
33
0
July 12, 2025
Uber
Hard
Data ScientistDesign an RCT for app-open discount
Analytics & Experimentation
5
0
October 13, 2025
Uber
Medium
Data ScientistEvaluate Auto-Play Impact with Key Metrics and Experiment Design
Analytics & Experimentation
25
0
July 12, 2025
Meta
Easy
Data ScientistAnalyze and mitigate fake advertiser accounts
Analytics & Experimentation
6
0
February 15, 2026
Netflix
Hard
Data ScientistPlan and analyze a ranking A/B test
Analytics & Experimentation
7
0
October 13, 2025
DoorDash
Medium
Data ScientistBoost App Installs: Analyze and Experiment with Conversion Funnel
Analytics & Experimentation
21
0
July 12, 2025
Common mistakes in root cause analysis
- Jumping to a hypothesis before confirming the data is correct
- Not segmenting the data to isolate the affected population
- Confusing correlation with causation
- Investigating too many hypotheses at once without prioritization
- Presenting findings without quantifying the impact
How root cause analysis is evaluated
Show a structured, systematic approach rather than random guessing.
Prioritize hypotheses by likelihood and ease of validation.
Communicate your investigation as a clear narrative with supporting data.
Related analytics concepts
Root Cause Analysis Interview FAQs
How do you investigate a metric drop?
First confirm it is real (check data pipelines). Then segment by dimensions (platform, country, cohort). Check for external factors and recent deployments. Decompose the metric into sub-components to isolate where the drop occurs. Quantify the impact and propose next steps.