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Updated weekly · Last refresh Sep 11

Ninja Analytics Data Scientist Interview Questions

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

17questions
~3htotal time
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1
Product SenseStart here. 3 questions · ~27 min
Choose the Right Feature MetricMedium

Framework for choosing whether a new feature should be measured primarily by engagement, retention, or revenue.

Feature PrioritizationUser NeedsProduct VisionNinja Analytics
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2
A/B Testing & Experimentation3 questions · ~27 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 MismatchNinja Analytics
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3
Behavioral & Leadership4 questions · ~36 min
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4
More topics7 questions · ~63 min
Running 30-Day Average in SQLMedium
Practice

Calculate GDIT's 30-day average completed transaction volume using daily aggregation and a PostgreSQL window frame.

Window FunctionssqlNinja Analytics
Determining A/B Test SignificanceMedium

Explain how to evaluate whether an A/B test result is statistically significant and how to interpret the result.

marketing experimentStatistical SignificanceAnalysisNinja Analytics
Diagnosing Metric DropsMedium

Assesses your structured approach to incident-style metric debugging and root-cause analysis.

root cause analysisperformance metricsNinja Analytics
Balancing Precision and RecallMedium

Tests your ability to tune anomaly detection models using classification trade-offs and decision thresholds.

anomaly detectionNinja Analytics
Cleaning Mission Data for EDAMedium

Evaluates your data wrangling skills for turning messy mission data into analysis-ready datasets.

data cleaningNinja Analytics
Handling Missing Data in SQLEasy

Explain practical SQL techniques for handling NULLs and missing values in product analysis without biasing metrics.

Data WranglingCase WhenAggregationsNinja Analytics
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Hands-on SQL practiceWrite and run real queries in the editor. 2 drills · ~20 min
SQL Rolling Churn With SeasonalityHard
Practice
Practice drill

Measure three-month customer activity and classify churn while adjusting for monthly seasonal expectations.

Window FunctionsDate FunctionsCase WhenAmazon DSPSyntes
Extract Trends With Window FunctionsMedium
Practice
Practice drill

Clean transaction records and rank users by monthly spending to reveal behavioral trends.

Window Functionsdata cleaningGroup ByIBM
The finish line: interview-readyComplete all 17 questions plus 2 hands-on drills to finish this plan.