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Updated weekly · Last refresh Oct 17

Steelcase Data Scientist Interview Questions

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

30questions
~4htotal time
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1
Machine LearningStart here. 4 questions · ~32 min
Handle Highly Imbalanced ClassesMedium

Build a classifier for a highly imbalanced dataset and choose training and evaluation methods that surface rare positives.

Cross-ValidationFeature EngineeringSupervised LearningSteelcase
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2
Product Sense3 questions · ~24 min
Prioritize Features for AI ProductsMedium

A framework for prioritizing AI product features based on user value, feasibility, evaluation quality, and trade-offs.

Feature PrioritizationValue PropositionSteelcase
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3
SQL & Data Manipulation5 questions + 3 drills · ~70 min
Write SQL to Select EntitiesEasy

Evaluates proficiency writing basic SQL for data retrieval tasks.

sqlSteelcase
SQL Join and Customer TotalsEasy
Practice
Practice drill

Use a LEFT JOIN and conditional aggregation to calculate completed sales totals for every Univar Solutions customer.

JoinsGroup ByAggregationsUnivar SolutionsSteelcase
Handling Missing Data in DashboardsMedium
Practice
Practice drill

Classify missing or corrupted dashboard metrics and expose only validated values for executive reporting.

missing valuesData Qualitydata accuracySoFiGeneral Dynamics Information Technology
Find Inactive TaskersMedium
Practice
Practice drill

Given two tables, Tasks and Taskers, write a query to find Taskers who have not completed a task in the last 3 months on TaskRabbit. Treat a task as compl...

SubqueriesDate Functionsnull handlingTaskRabbit
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4
Model Evaluation3 questions · ~24 min
Cross Validation PurposeMedium

Tests understanding of model evaluation methodology and generalization.

Steelcase
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5
Behavioral & Leadership8 questions · ~64 min
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6
More topics7 questions · ~56 min
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 IndicatorsDiagnosisSteelcase
Monitor Feature Drift in Demand PredictionHard

Tests monitoring strategy, drift detection, and operational readiness for production models.

Feature StoreFeature DriftModel ServingSteelcase
Decision Trees for Non-Technical StakeholdersEasy

Tests communication clarity and ability to translate model mechanics into business-friendly language.

Decision Makingsoft skillsCommunicationSteelcase
Significance of Utilization ChangeMedium

Tests statistical testing choices for detecting meaningful changes in Steelcase workplace utilization metrics.

Confidence IntervalsHypothesis TestingStatistical SignificanceSteelcase
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The finish line: interview-readyComplete all 30 questions plus 3 hands-on drills to finish this plan.