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Updated weekly · Last refresh Sep 20
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Arup Data Scientist Interview Questions

The questions to prepare for a Arup Data Scientist interview. Questions from real interview reports rank first. Updated daily.

11questions
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1
A/B Testing & ExperimentationStart here. 3 questions · ~24 min
Common Pitfalls in Experiment ResultsHard

Identify the main pitfalls that can distort A/B test interpretation and explain how to guard against them.

PeekingNovelty EffectSample Ratio MismatchAArup
Designing Bias-Minimized A/B TestsMedium

Evaluates experimental design choices that reduce bias and confounding.

experiment designA/B TestingAArup
Statistical Significance with Small SamplesMedium

Tests understanding of power, variance, and significance under limited data conditions.

Statistical SignificanceAArup
2
Behavioral & Leadership4 questions · ~32 min
Resolving Methodology DisagreementMedium

Tests conflict resolution in an analytical setting, especially how you use data, communication, and consensus-building to resolve methodology disputes.

Conflict ResolutionteamworkmethodologyAArup
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3
More topics4 questions · ~32 min
Handling Missing and Dirty SQL DataMedium

Explain how to profile, clean, and standardize missing or dirty data before analysis.

Data WranglingCase WhenQualityAArup
Prioritize Features for a New ProductMedium

A framework for deciding which features should ship first when building a new product.

Feature PrioritizationUser Needsproduct developmentAArup
Diagnosing Engagement Metric DropsMedium

Assesses structured debugging of metric changes and root-cause analysis.

Data AnalysisProblem SolvingAArup
Measuring Sustainability Dashboard SuccessMedium

Tests ability to define actionable product metrics tied to impact at Arup.

sustainabilityUser NeedsAArup

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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
Handling 30% Missing Critical DataMedium
Practice
Practice drill

Audit critical-field completeness by application source and report missing-entry percentages.

missing valuesdata accuracyData WranglingAmerican Credit Acceptance
Normalize Messy CRM Contact DataHard
Practice
Practice drill

Clean inconsistent CRM contacts by joining source tables, standardizing values, and flagging bad records.

Data WranglingCase WhenQualityAlphaSense
Validate Monthly Revenue Data QualityHard
Practice
Practice drill

Use joins, a CTE, and CASE logic to flag messy monthly order data and produce cleaned revenue by month.

Data WranglingCase WhenQualityLiterati
The finish line: interview-readyComplete all 11 questions plus 3 hands-on drills to finish this plan.