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Updated weekly · Last refresh Sep 20
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Battelle Data Scientist Interview Questions

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

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~2htotal time
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1
A/B Testing & ExperimentationStart here. 3 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 MismatchBBattelle
Investigating Experiment vs MetricsMedium

Tests your approach to diagnosing metric inconsistencies between experiment outcomes and product KPIs.

MetricsData AnalysisBBattelle
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2
Behavioral & Leadership4 questions · ~36 min
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3
More topics6 questions · ~54 min
SQL Window Functions Moving AverageMedium
Practice

Calculate a three-day moving average of active-client revenue using aggregation and SQL window functions.

Window FunctionsData AnalysissqlBBattelle
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 IndicatorsDiagnosisBBattelle
Designing Success MetricsMedium

Assesses your ability to define measurable outcomes aligned to user value and research goals at Battelle.

product metricsdesignsuccess metricsBBattelle
Statistical Significance in A/BMedium

Tests ability to interpret statistical significance for product decisions.

Statistical SignificanceA/B TestingBBattelle
Handling Missing and Dirty SQL DataMedium

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

Data WranglingCase WhenQualityBBattelle
Balancing Long-Term and EngagementMedium

Assesses your ability to choose and trade off metrics that reflect both durable value and near-term behavior.

user valuebalanceEngagement MetricsBBattelle

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Hands-on SQL practiceWrite and run real queries in the editor. 2 drills · ~20 min
Running Average With Window FunctionsEasy
Practice
Practice drill

Calculate each Hinge user's 30-day rolling average of daily interactions using CTEs and window functions.

Window FunctionsData AnalysissqlHingeCCatalyst Operations & AnalyticsAAlight Solutions
Using SQL Window FunctionsMedium
Practice
Practice drill

Calculate three-day rolling passenger entry averages for TfL stations using aggregation and window functions.

Window FunctionsData AnalysissqlTransport for LondonThe Trade DeskMercari
The finish line: interview-readyComplete all 13 questions plus 2 hands-on drills to finish this plan.