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Billie Data Scientist Interview Questions

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

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~3htotal time
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1
Machine LearningStart here. 7 questions · ~56 min
Handling Imbalanced Fraud LabelsMedium

Explain how to train and evaluate models on highly imbalanced fraud data without relying on misleading accuracy.

Cross-ValidationFeature EngineeringSupervised LearningBBillie
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2
System Design3 questions · ~24 min
Integrating New Data SourcesMedium

Tests data integration, feature consistency, and safe model updates in production.

data integrationBBillie
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3
Behavioral & Leadership4 questions · ~32 min
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4
More topics11 questions · ~88 min
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 IndicatorsDiagnosisBBillie
Evaluate Models in ProductionHard

How to evaluate a production model using calibration, thresholds, and confusion matrix tradeoffs.

CalibrationAccuracyThreshold TuningBBillie
Causal Test for Feature X ImpactHard

Design an experiment to determine whether feature X causally changes metric Y, with power, guardrails, and a pre-registered decision rule.

ExperimentationCausal InferenceA/B TestingBBillie
Prioritize Features for AI ProductsMedium

A framework for prioritizing AI product features based on user value, feasibility, evaluation quality, and trade-offs.

Feature PrioritizationValue PropositionBBillie
Ensuring Statistical SignificanceMedium

Tests rigor in experimental design and interpreting statistical significance correctly.

experiment designStatistical SignificanceBBillie
Orchestration with Airflow or MetaflowMedium

Tests your practical experience orchestrating data and ML workflows reliably.

data pipelinesOrchestrationBBillie
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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
SQL Data Quality ChecksHard
Practice
Practice drill

Detect missing fields, duplicate transaction keys, invalid amounts, and merchant reference issues with PostgreSQL CTEs and joins.

Data Qualitynull handlingdata integritySigmoid
Identify Funnel Bottlenecks in OnboardingHard
Practice
Practice drill

Use joins, CTEs, and aggregations to find the weakest step in an onboarding funnel and estimate lost conversions.

Funnel AnalysissqlDiagnosisAtlassianRevolut
SQL Missing Marks RuleEasy
Practice
Practice drill

Use GROUP BY and HAVING to exclude students missing marks in two or more subjects.

data cleaningsqlPayPal
The finish line: interview-readyComplete all 25 questions plus 3 hands-on drills to finish this plan.