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Updated weekly · Last refresh Sep 25

Kohl's Data Scientist Interview Questions

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

19questions
~3htotal time
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1
A/B Testing & ExperimentationStart here. 3 questions · ~27 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 MismatchKohl's
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2
Product Sense5 questions · ~44 min
Define Recommendation Engine MetricMedium

Assesses metric design for personalization quality and business impact.

user experienceRecommendation SystemsKohl's
Measure Kohl's App Feature SuccessMedium

Tests defining success metrics and instrumentation for a Kohl's customer-facing feature.

feature evaluationuser engagementsuccess metricsKohl's
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3
Behavioral & Leadership5 questions · ~44 min
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4
More topics6 questions · ~53 min
SQL Window Functions Rolling AverageMedium
Practice

Calculate three-day rolling average sales by region using aggregation, joins, and PostgreSQL window functions.

Window FunctionssqlKohl's
Investigate Metric DropMedium

Diagnose a below-expectations product metric drop using decomposition, guardrails, and experiment checks.

metric selectionDiagnosiskpi hierarchyKohl's
Statistical Significance in Hypothesis TestingEasy

Explain what statistical significance means and why it matters when interpreting experimental or analytical results.

Hypothesis TestingData AnalysisStatistical SignificanceKohl's
Handling Missing Customer DataMedium

Explain how to detect, classify, and handle missing customer data in PostgreSQL using NULL handling and validation logic.

data cleaningData Analysisdata integrityKohl's
Define Success for a ProjectEasy

Define what success means for a project using clear KPIs, a north star, and supporting metrics.

KPIsSuccess CriteriaDiagnosisKohl's
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Hands-on SQL practiceWrite and run real queries in the editor. 2 drills · ~20 min
SQL Window Functions Moving AverageMedium
Practice
Practice drill

Calculate a three-day moving average of active-client revenue using aggregation and SQL window functions.

Window FunctionsData AnalysissqlAAreteEEvalueserveSShyena Tech Yarns
Running Average With Window FunctionsEasy
Practice
Practice drill

Calculate each Hinge user's 30-day rolling average of daily interactions using CTEs and window functions.

Window FunctionsData AnalysissqlHingeCCatalyst Operations & AnalyticsAAlight Solutions
The finish line: interview-readyComplete all 19 questions plus 2 hands-on drills to finish this plan.