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United States Steel Data Scientist Interview Questions

The questions to prepare for a United States Steel Data Scientist interview. Questions from real interview reports rank first. Updated weekly.

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~3htotal time
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1
Machine LearningStart here. 4 questions · ~32 min
Supervised vs Unsupervised LearningEasy

Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.

Unsupervised LearningFeature EngineeringBias-Variance TradeoffUnited States Steel
Describe an ML Project You BuiltMedium

Describe a machine learning project, from problem framing and feature work to model training and evaluation.

Cross-ValidationFeature EngineeringSupervised LearningUnited States Steel
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2
Metrics4 questions · ~32 min
Analysis That Drove Measurable ImpactEasy

Describe a case where your analysis used the right metrics, shaped a decision, and produced a meaningful business result.

KPIsLeading IndicatorsDiagnosisUnited States Steel
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3
Statistics & Probability5 questions · ~40 min
Linear Regression ImplementationEasy

Tests your ability to build and reason about a core statistical model.

RegressionCorrelationHypothesis TestingUnited States Steel
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4
More topics6 questions · ~48 min
Validate Statistical Model ReliabilityMedium

Explain how to validate a statistical model so its performance is reliable, stable, and useful in practice.

Cross-ValidationCalibrationAccuracyUnited States Steel
Incomplete Data Analysis ProjectEasy

Tests your approach to missingness, robustness checks, and producing defensible insights.

Data ModelingBackfillingQualityUnited States Steel
Role of a Data ScientistMedium

Tests your understanding of how data science supports business decisions at United States Steel.

User NeedsValue PropositionProduct VisionUnited States Steel
Handling Missing DataEasy

Tests your data preprocessing judgment and impact awareness on downstream models.

Data WranglingETLQualityUnited States Steel
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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
Monthly Sales Trends by CategoryMedium
Practice
Practice drill

Aggregate monthly sales by product category and use LAG to calculate month-over-month changes.

InfrastructureToolsData WranglingTotal Wine & MoreInc.Benjamin Moore
Handle Missing Financial Input ValuesMedium
Practice
Practice drill

Use joins, CTEs, and CASE logic to flag and fill missing financial fields for Qlik planning records.

Data WranglingETLQualityQlik
Contact Center Agent Performance MetricsHard
Practice
Practice drill

Compute daily agent call KPIs and SLA using joins, aggregations, and window ranking in a contact center model.

ETLAggregationsData ModelingADP
The finish line: interview-readyComplete all 19 questions plus 3 hands-on drills to finish this plan.