Raptive Data Scientist Interview Questions

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

Prevent Overfitting in ML Models

Easy

Explain how to reduce overfitting using regularization, validation, and model selection.

Cross-ValidationBias-Variance TradeoffRegularization

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A/B Testing for Product Features

Medium

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 Testing

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Mean and Standard Deviation Basics

Easy

Compute the mean and standard deviation of a dataset, and distinguish the sample standard deviation from the population version.

DistributionsVarianceExpected Value

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Handling Missing Data in SQL

Easy

Explain how to identify, assess, and handle missing values in SQL using NULL checks, COALESCE, and validation logic.

Data WranglingCase WhenQuality

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Choosing Classification Evaluation Metrics

Easy

Explain which classification metrics to use and how metric choice depends on the business objective and error tradeoffs.

PrecisionAccuracyRecall

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User Churn Modeling Plan

Hard

Tests your end-to-end modeling process, from data to evaluation, for churn prediction.

RetentionChurnLTV

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