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Fundbox Data Scientist Interview Questions

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

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1
Machine LearningStart here. 8 questions · ~64 min
Bagging vs Boosting ExplainedMedium

Explain how bagging and boosting differ, and identify a representative algorithm for each ensemble method.

Ensemble Methodsmodel trainingSupervised LearningFundbox
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2
Behavioral & Leadership5 questions · ~40 min
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3
More topics10 questions · ~80 min
Debug Training to Production GapHard

Approach for debugging a model that looks strong offline but fails after deployment.

Cross-ValidationCalibrationPrecisionFundbox
Design Real-Time Fraud Risk ScoringHard

Design a real-time fraud scoring system for card transactions with strict latency, delayed labels, and high availability requirements.

Feature StoreFeature DriftModel ServingFundbox
Statistical Significance in CreditEasy

Tests understanding of hypothesis testing and interpreting results for credit decisions.

Hypothesis TestingStatistical SignificanceP-ValuesFundbox
Pitfalls in Credit ExperimentsHard

Tests ability to anticipate bias, confounding, and measurement issues in credit experiments.

experiment designNetwork InterferenceSample Ratio MismatchFundbox
Rolling 30-Day Default Rate in SQLMedium

Tests SQL window function proficiency for time-based credit metrics.

Window FunctionssqlRunning TotalsFundbox
Lending Product Metrics and GuardrailsMedium

Tests ability to select credit, approval, and risk metrics and set safe operating guardrails.

MetricsGuardrail MetricsProduct VisionFundbox
Diagnose Approval Rate DropsMedium

Tests structured troubleshooting using metrics, segmentation, and risk indicators.

Diagnosiskpi hierarchyFundbox
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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
Cumulative Comments by UserHard
Practice
Practice drill

Calculate each user's daily and cumulative Reddit comment count, including dates with no activity.

Window Functionsmissing valuesDate FunctionsReddit
Handling Missing DataMedium
Practice
Practice drill

Standardize missing observation fields with fallback values, missingness flags, and ordered output.

Window Functionsmissing valuesdata cleaningEversource EnergyCincinnati Children's HospitalAdtalem Global Education
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 AnalysissqlEEvalueserveSShyena Tech YarnsAATB Financial
The finish line: interview-readyComplete all 23 questions plus 3 hands-on drills to finish this plan.