Dataford
Top 19
Prep plan
Updated weekly · Last refresh Sep 18
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N26 Data Scientist Interview Questions

The questions to prepare for a N26 Data Scientist interview. Questions from real interview reports rank first. Updated daily.

19questions
~3htotal time
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1
Product SenseStart here. 3 questions · ~24 min
Define Savings Feature SuccessMedium

Define success for a feature meant to increase member savings behavior.

Feature PrioritizationUser NeedsValue PropositionNN26
Balance Conversion and RetentionHard

Framework for deciding when to favor short-term conversion gains versus long-term retention in a product decision.

Feature PrioritizationUser NeedsValue PropositionNN26
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2
A/B Testing & Experimentation3 questions · ~24 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 MismatchNN26
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3
Statistics & Probability3 questions · ~24 min
Ensuring Statistical SignificanceMedium

Evaluates rigor in hypothesis testing and result validation.

Statistical SignificanceAnalysisdata integrityNN26
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4
Machine Learning3 questions · ~24 min
Address Selection Bias and OverfittingMedium

Evaluates understanding of model generalization and bias in ML workflows.

Model EvaluationoverfittingNN26
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5
Behavioral & Leadership4 questions · ~32 min
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6
More topics3 questions · ~24 min
Handling Missing and Dirty SQL DataMedium

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

Data WranglingCase WhenQualityNN26
Define Success Metrics for FeaturesMedium

Set a clear north star, supporting KPIs, leading indicators, and guardrails for a new product feature.

North Star MetricLagging IndicatorsKPIsNN26
Investigate User Engagement DeclineMedium

Investigate a 15% engagement decline by decomposing the metric, isolating root causes, and proposing actions.

RetentionDiagnosisEngagement MetricsNN26

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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
SQL Conversion Rate Pre/Post LaunchMedium
Practice
Practice drill

Use CTEs and aggregations to compare product conversion rates before and after each Kovai product launch.

Conversion RateCTEsCase WhenKovai
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
The finish line: interview-readyComplete all 19 questions plus 3 hands-on drills to finish this plan.