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Updated weekly · Last refresh Sep 22

Caltech (California) Data Scientist Interview Questions

The questions to prepare for a Caltech (California) Data Scientist interview. Questions from real interview reports rank first. Updated daily.

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1
A/B Testing & ExperimentationStart here. 3 questions · ~28 min
Directionally Useful but Inconclusive TestHard

Decide whether to act on an A/B result that trends positive but is not statistically conclusive.

ExperimentationStatistical SignificanceA/B TestingCaltech (California)
Design Test for New FeatureMedium

Design an A/B test for a new platform feature, including success metrics, power, guardrails, and a clear ship decision.

experiment designfeature evaluationA/B TestingCaltech (California)
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 MismatchCaltech (California)
2
Behavioral & Leadership4 questions · ~38 min
Fixing Data Process InefficienciesMedium

Tests ownership, analytical judgment, and initiative in improving an inefficient data process.

initiativeefficiencyaccountabilityCaltech (California)
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3
More topics4 questions · ~38 min
Running Average With Window FunctionsEasy
Practice

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

Window FunctionsData AnalysissqlCaltech (California)
Root Cause for Metric DropHard

Identify whether a sudden grid dashboard metric drop reflects a real operational change, data-quality issue, or dashboard defect.

dashboardsroot cause analysisoperational metricsCaltech (California)
Statistical vs Practical SignificanceMedium

Explain why a statistically significant experiment result may still be too small to matter for product or business decisions.

Confidence IntervalsExperimentationHypothesis TestingCaltech (California)
Optimize Large PostgreSQL Query PerformanceMedium

Explain how to tune slow PostgreSQL queries on multi-million-row tables using indexes, execution plans, joins, and partitioning.

Performance Tuningquery optimizationsqlCaltech (California)

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Hands-on SQL practiceWrite and run real queries in the editor. 2 drills · ~20 min
SQL Window Functions Moving AverageMedium
Practice
Practice drill

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

Window FunctionsData AnalysissqlAAreteEEvalueserveSShyena Tech Yarns
Optimizing Slow SQL QueriesHard
Practice
Practice drill

Optimize a PostgreSQL transaction query that filters, aggregates, ranks, and returns top merchants efficiently on large tables.

Performance Tuningdatabase optimizationlarge datasetsSprinter HealthSentiLinkAutodesk
The finish line: interview-readyComplete all 11 questions plus 2 hands-on drills to finish this plan.