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Updated weekly · Last refresh Aug 30

T Mobile Us Data Scientist Interview Questions

The questions to prepare for a T Mobile Us Data Scientist interview. Questions from real interview reports rank first. Updated weekly.

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1
Product SenseStart here. 3 questions · ~24 min
Segment Customers for Campaign TargetingMedium
Recently asked

Approach for defining actionable customer segments for a targeted marketing campaign and choosing the right trade-offs.

User SegmentsFeature PrioritizationUser NeedsT Mobile Us
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2
Statistics & Probability3 questions · ~24 min
Sample Size and Power PlanningMedium
Recently asked

Reason about sample size, power, and minimum detectable effect before launching an experiment.

Hypothesis TestingPower AnalysisSample SizeT Mobile Us
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3
Machine Learning3 questions · ~24 min
Feature Engineering for Predictive ModelsMedium
Recently asked

Explain how to choose, transform, and validate features for a predictive model using a structured ML workflow.

Cross-ValidationFeature EngineeringSupervised LearningT Mobile Us
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4
Behavioral & Leadership7 questions · ~57 min
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5
More topics10 questions · ~82 min
Diagnose a Metric Drop After LaunchMedium
Recently asked

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

Lagging IndicatorsLeading IndicatorsDiagnosisT Mobile Us
Resolve Conflicting Metric Test ResultsHard
Recently asked

Design an experiment where a workflow change lifts containment but may hurt CSAT, and define a ship rule that respects guardrails.

ExperimentationGuardrail MetricsA/B TestingT Mobile Us
SQL Window Functions for ChurnMedium
Recently asked

Tests practical SQL skills for time-based churn analysis using window functions.

Window FunctionsChurnsqlT Mobile Us
Real-Time Churn Prediction DeploymentHard
Recently asked

Tests end-to-end deployment design for real-time churn prediction in a telecom setting.

ChurnModel ServingdeploymentT Mobile Us
Unsupervised Clustering EvaluationMedium
Recently asked

Tests evaluation strategy for unsupervised clustering without labeled data.

ClusteringEvaluation TechniquesModel MetricsT Mobile Us
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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 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 AnalysissqlAAreteMITREGlassdoor
Rolling 7-Day Incident AverageMedium
Practice
Practice drill

Calculate a calendar-aware 7-day average of Samsara incident counts using CTEs and window functions.

Window FunctionsData AnalysissqlSamsara
Rolling 7-Day Average in SQLMedium
Practice
Practice drill

Calculate calendar-aware 7-day sensor anomaly averages per Mercedes-Benz vehicle using daily aggregation and window functions.

Window FunctionsData AnalysissqlMercedes-Benz Group
The finish line: interview-readyComplete all 26 questions plus 3 hands-on drills to finish this plan.