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Tempus labs Data Scientist Interview Questions

The questions to prepare for a Tempus labs Data Scientist interview. Questions from real interview reports rank first. Updated weekly.

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1
Machine LearningStart here. 8 questions · ~64 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 TradeoffTempus labs
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2
SQL & Data Manipulation3 questions + 3 drills · ~54 min
Handling Missing and Dirty SQL DataMedium

Explain how to profile, clean, and standardize missing or dirty data before analysis.

Data WranglingCase WhenQualityTempus labs
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
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3
Statistics & Probability5 questions · ~40 min
Biases in Take-Home DatasetMedium

Tests your ability to identify dataset bias and its impact on fairness and validity for Tempus labs analyses.

Tempus labs
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4
Behavioral & Leadership14 questions · ~112 min
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5
More topics7 questions · ~56 min
First Checks for Metric DropsEasy

Outline the first checks to diagnose a sudden drop in a core product metric, starting with data quality, scope, and decomposition.

Lagging IndicatorsLeading IndicatorsDiagnosisTempus labs
Best Practices for Model EvaluationMedium

Explain the best practices for evaluating a model and choosing metrics that match the task.

PrecisionAccuracyRecallTempus labs
Design Test for New FeatureMedium

Design an A/B test for a new platform feature, including success metrics, power, guardrails, and a clear ship decision.

experiment designfeature evaluationA/B TestingTempus labs
Prioritize Features for AI ProductsMedium

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

Feature PrioritizationValue PropositionTempus labs
Deploying for Real-Time Clinical DataHard

Tests system design tradeoffs for low-latency, reliability, and safety when serving Tempus labs clinical decisions.

real-time dataTempus labs
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The finish line: interview-readyComplete all 37 questions plus 3 hands-on drills to finish this plan.