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Updated weekly · Last refresh Sep 18
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Novartis HealthCare Data Scientist Interview Questions

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

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~3htotal time
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1
MetricsStart here. 3 questions · ~25 min
Analyze New Feature EngagementMedium

Assess a new feature using adoption, activation, repeat usage, and retention metrics tied to user value.

Leading IndicatorsA/B TestingEngagement MetricsNNovartis HealthCare
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 IndicatorsDiagnosisNNovartis HealthCare
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2
A/B Testing & Experimentation3 questions · ~25 min
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 MismatchNNovartis HealthCare
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3
Machine Learning5 questions · ~41 min
Walk Through a Technical ProjectMedium

Evaluates your ability to communicate problem framing, modeling choices, and outcomes.

Model EvaluationNNovartis HealthCare
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4
Behavioral & Leadership4 questions · ~33 min
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5
More topics7 questions · ~57 min
Power Analysis for Experiment PlanningMedium

Reason about power analysis when planning an experiment and choosing sample size.

ExperimentationPower AnalysisSample SizeNNovartis HealthCare
Handling Missing and Dirty SQL DataMedium

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

Data WranglingCase WhenQualityNNovartis HealthCare
Check Model PerformanceMedium

Assesses your approach to model evaluation and selecting appropriate metrics.

Evaluation Techniquesmodel performanceNNovartis HealthCare
Ensuring ReproducibilityMedium

Tests practices for repeatable results, versioning, and reliable analysis pipelines.

best practicesreproducibilityData AnalysisNNovartis HealthCare
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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
Handling 30% Missing Critical DataMedium
Practice
Practice drill

Audit critical-field completeness by application source and report missing-entry percentages.

missing valuesdata accuracyData WranglingAmerican Credit Acceptance
Normalize Messy CRM Contact DataHard
Practice
Practice drill

Clean inconsistent CRM contacts by joining source tables, standardizing values, and flagging bad records.

Data WranglingCase WhenQualityAlphaSense
Validate Monthly Revenue Data QualityHard
Practice
Practice drill

Use joins, a CTE, and CASE logic to flag messy monthly order data and produce cleaned revenue by month.

Data WranglingCase WhenQualityLiterati
The finish line: interview-readyComplete all 22 questions plus 3 hands-on drills to finish this plan.