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Updated weekly · Last refresh Aug 30

H&R Block Data Scientist Interview Questions

The questions to prepare for a H&R Block Data Scientist interview. Questions from real interview reports rank first. Updated weekly.

35questions
~5htotal time
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1
Model EvaluationStart here. 3 questions · ~24 min
Choose Metrics for Imbalanced ClaimsMedium

Select the right metric for a fraud-claim model with 1.8% positives, high accuracy, low recall, and sharply asymmetric false-negative costs.

PrecisionAUC-ROCRecallH&R Block
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2
Pipelines4 questions · ~32 min
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityH&R Block
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3
Statistics & Probability3 questions · ~24 min
A/B Testing for Product FeaturesMedium

Explain how to design and evaluate an A/B test for a product feature, including metrics, MDE, sample size, and guardrails.

Hypothesis TestingStatistical SignificanceA/B TestingH&R Block
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4
Machine Learning14 questions · ~114 min
Propensity for Professional Tax ServicesMedium

Tests modeling approach for propensity targeting and marketing effectiveness.

Cross-ValidationFeature EngineeringSupervised LearningH&R Block
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5
Product Sense5 questions · ~41 min
Modeling Drop-off and InterventionsHard

Tests funnel analytics, modeling, and actionability for personalized product interventions.

Feature PrioritizationPain PointsUse CasesH&R Block
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6
More topics6 questions · ~49 min
Advanced SQL for Data ExtractionEasy

Tests your SQL proficiency for extracting reliable datasets for analysis and modeling.

JoinsGroup ByAggregationsH&R Block
Predicting Customer ChurnMedium

Tests churn modeling approach and how you operationalize predictions.

ChurnDiagnosisLTVH&R Block
Deep Learning for Financial DocumentsHard

Tests deep learning capability for document understanding in financial contexts.

Language ModelsText ClassificationDeep LearningH&R Block
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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
Monthly Sales Trends by CategoryMedium
Practice
Practice drill

Aggregate monthly sales by product category and use LAG to calculate month-over-month changes.

InfrastructureToolsData WranglingTotal Wine & MoreInc.Benjamin Moore
Contact Center Agent Performance MetricsHard
Practice
Practice drill

Compute daily agent call KPIs and SLA using joins, aggregations, and window ranking in a contact center model.

ETLAggregationsData ModelingADP
30-Day Moving Average of BookingsMedium
Practice
Practice drill

Build a daily series and compute a 30-day trailing moving average of task bookings using window functions.

Window FunctionsData WranglingAggregationsTaskRabbitRBC
The finish line: interview-readyComplete all 35 questions plus 3 hands-on drills to finish this plan.