Top 22

Prep plan

Updated weekly · Last refresh Oct 17

PROS Data Scientist Interview Questions

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

22questions

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Machine LearningStart here. 4 questions · ~36 min

Feature Selection TechniquesHard

Compare filter, wrapper, and embedded feature selection methods while preserving validation performance and interpretability.

Cross-Validationmodel selectionFeature Engineering

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Statistics & Probability3 questions · ~27 min

Maximum Likelihood EstimationMedium

Assesses your understanding of maximum likelihood estimation and practical parameter estimation.

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Behavioral & Leadership6 questions · ~55 min

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More topics9 questions · ~82 min

Analyze Customer Purchase Trends with Window FunctionsEasy

Practice

Calculate the monthly spending trends for customers using window functions and joins.

PROS

Pricing Page Experiment DesignHard

Design an end-to-end A/B test for a pricing page, including MDE, guardrails, analysis plan, and a ship decision.

Guardrail MetricsSample SizeA/B Testing

PROS

Evaluate Regression with RMSE and MAEEasy

Explain how to evaluate a regression model with RMSE and MAE, and how to interpret the tradeoff between average and large errors.

RegressionMAERMSE

PROS

Turn Feedback Into New FeaturesMedium

Approach for turning user feedback into a well-scoped feature, with clear prioritization, MVP definition, and success metrics.

User ResearchFeature PrioritizationUser Needs

PROS

Diagnose KPI Drop After ReleaseMedium

Diagnose a post-release KPI drop by separating instrumentation issues from real behavior changes and tracing the problem through the metric hierarchy.

KPILeading IndicatorsDiagnosis

PROS

Optimizing Algorithm PerformanceMedium

Tests your ability to improve runtime and accuracy using profiling, tuning, and algorithmic tradeoffs.

CommunicationOwnershipPrioritization

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Hands-on SQL practiceWrite and run real queries in the editor. 1 drill · ~10 min

Handling Missing ValuesMedium

Practice

Practice drill

Create an auditable application dataset by applying explicit PostgreSQL rules for missing candidate, status, salary, and date values.

missing valuesdata cleaningnull handling

Aircall

athenahealth

Hays

The finish line: interview-readyComplete all 22 questions plus 1 hands-on drill to finish this plan.