Top 17

Prep plan

Updated weekly · Last refresh Oct 30

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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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 Vision

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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 Mismatch

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Behavioral & Leadership4 questions · ~36 min

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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 Functionssql

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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 SignificanceAnalysis

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Diagnosing Metric DropsMedium

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

root cause analysisperformance metrics

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Balancing Precision and RecallMedium

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

anomaly detection

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Cleaning Mission Data for EDAMedium

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

data cleaning

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Handling Missing Data in SQLEasy

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

Data WranglingCase WhenAggregations

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Hands-on SQL practiceWrite and run real queries in the editor. 2 drills · ~20 min

Handling Missing DataEasy

Practice

Practice drill

Replace missing sales fields with defined defaults and return a consistently ordered dataset.

missing valuesdata cleaningnull handling

University of Minnesota

The Home Depot

Nationwide

Handle Dataset DuplicatesMedium

Practice

Practice drill

Identify repeated Epsilon event records by business-key values and report duplicate and removable row counts.

data cleaningData WranglingCase When

Epsilon

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