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Loblaw Companies Data Scientist Interview Questions

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

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~2htotal time
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1
MetricsStart here. 3 questions · ~28 min
Investigate Metric DropMedium

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

metric selectionDiagnosiskpi hierarchyLLoblaw Companies
Define a Product North StarMedium

Define a North Star Metric for a product and explain how it guides KPI selection and growth decisions.

User SegmentsNorth Star MetricKPIsLLoblaw Companies
Diagnose a Metric Drop After LaunchMedium

Investigate why a key KPI moved the wrong way after a product change and separate signal from noise.

Lagging IndicatorsLeading IndicatorsDiagnosisLLoblaw Companies
2
Behavioral & Leadership5 questions · ~47 min
Why This Company, Why NowEasy

Tests motivation, company fit, and whether the candidate can connect their career goals to customer impact and the product mission.

self assessmentfitmotivationLLoblaw Companies
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3
More topics4 questions · ~37 min
SQL Window Functions Moving AverageMedium
Practice

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

Window FunctionsData AnalysissqlLLoblaw Companies
Statistical Significance in Hypothesis TestingEasy

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

Hypothesis TestingData AnalysisStatistical SignificanceLLoblaw Companies
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 MismatchLLoblaw Companies
Understanding Database Indexing PurposeMedium

Explain the purpose of using indexes in databases and their impact on query performance.

LLoblaw Companies

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Hands-on SQL practiceWrite and run real queries in the editor. 2 drills · ~20 min
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
Using SQL Window FunctionsMedium
Practice
Practice drill

Calculate three-day rolling passenger entry averages for TfL stations using aggregation and window functions.

Window FunctionsData AnalysissqlTransport for LondonThe Trade DeskMercari
The finish line: interview-readyComplete all 12 questions plus 2 hands-on drills to finish this plan.