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AbCellera Data Scientist Interview Questions

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

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1
A/B Testing & ExperimentationStart here. 4 questions · ~32 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 MismatchAAbCellera
Experiment Metric PitfallsMedium

Evaluates experience avoiding common errors in experimental metric design.

AAbCellera
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2
Behavioral & Leadership3 questions · ~24 min
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3
More topics7 questions · ~56 min
Handling Missing and Dirty SQL DataMedium

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

Data WranglingCase WhenQualityAAbCellera
Investigate User Engagement DeclineMedium

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

RetentionDiagnosisEngagement MetricsAAbCellera
Speed vs Accuracy Trade-offsMedium

Assesses decision-making for balancing latency and correctness in product analytics.

Trade-offsAAbCellera
Explain Power AnalysisMedium

Evaluates ability to communicate statistical concepts clearly to non-technical audiences.

Power AnalysisAAbCellera
High-Throughput Process MetricsMedium

Tests ability to define success metrics for AbCellera’s high-throughput screening workflows.

AAbCellera
Significance With Small SamplesMedium

Assesses statistical reasoning for inference under limited data.

Statistical SignificanceAAbCellera
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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
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
Validate Monthly Revenue Data QualityHard
Practice
Practice drill

Use joins, a CTE, and CASE logic to flag messy monthly order data and produce cleaned revenue by month.

Data WranglingCase WhenQualityLiterati
The finish line: interview-readyComplete all 14 questions plus 3 hands-on drills to finish this plan.