Dataford
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Updated weekly · Last refresh Aug 30

Fanning Personnel Data Scientist Interview Questions

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

25questions
~3htotal time
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1
System DesignStart here. 3 questions · ~24 min
Design Feature Drift Monitoring SystemHard

Design a production ranking system with robust feature drift monitoring across batch and real-time features at high QPS.

Feature StoreFeature DriftModel ServingFanning Personnel
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2
A/B Testing & Experimentation4 questions · ~33 min
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 TestingFanning Personnel
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3
Machine Learning3 questions · ~24 min
Overfitting in Supervised LearningMedium

Explain how to diagnose and reduce overfitting using validation strategy, regularization, and model complexity control.

Feature EngineeringDeep LearningSupervised LearningFanning Personnel
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4
Behavioral & Leadership6 questions · ~49 min
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5
More topics9 questions · ~73 min
Validate a Machine Learning ModelEasy

How to validate a machine learning model and interpret whether its metrics are trustworthy.

PrecisionAccuracyRecallFanning Personnel
Diagnose a Metric Drop After LaunchMedium

Investigate why a key KPI moved the wrong way after a product change and separate signal from noise.

Lagging IndicatorsLeading IndicatorsDiagnosisFanning Personnel
Choosing a Significance TestEasy

Explain how to choose an appropriate significance test based on metric type, study design, and the null hypothesis.

Confidence IntervalsHypothesis TestingStatistical SignificanceFanning Personnel
Rules vs AI Trade-offsMedium

Tests your ability to reason about cost, accuracy, maintainability, and risk when choosing solution approaches.

Value PropositionUse CasesProduct VisionFanning Personnel
7-Day Rolling Average ComparisonMedium

Tests SQL window function proficiency for time-series aggregation and period-over-period comparison.

Window FunctionsLag/LeadRunning TotalsFanning Personnel
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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
SQL Rolling Average with WindowsMedium
Practice
Practice drill

Use PostgreSQL window functions to compare Harnham Recruiter Cloud's seven-day application average with the prior week.

Window FunctionsLag/LeadRunning TotalsHarnhamHach
User Ride Trends with WindowsMedium
Practice
Practice drill

Use monthly aggregation and LAG to measure each rider’s completed ride trend and month-over-month change.

Window FunctionsLag/LeadRunning TotalsDidi ChuxingUber
Track Monthly Customer Activity ChangesMedium
Practice
Practice drill

Use window functions to compare each customer's monthly activity against prior months and identify behavior changes.

Window FunctionsLag/LeadRunning TotalsQantas
The finish line: interview-readyComplete all 25 questions plus 3 hands-on drills to finish this plan.