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Prep plan
~2h total · Updated weekly · Last refresh Aug 9

Shoptaki Data Scientist Interview Questions

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

Supervised vs Unsupervised LearningEasy

Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.

Unsupervised LearningFeature EngineeringBias-Variance Tradeoff
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Bias-Variance Tradeoff in Practice
Medium

Explain the bias-variance tradeoff and how it guides model choice, regularization, and generalization performance.

Cross-ValidationBias-Variance TradeoffRegularization
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Motivation for Data Engineering WorkEasy

Explain what drives your interest in data engineering, grounded in user needs and the value created by reliable data systems.

Jobs to Be DoneUser NeedsValue Proposition
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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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Linear Regression Assumptions (Basics)
Easy

Tests foundational knowledge of linear regression assumptions and how they affect inference.

Regressionassumptions
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Diagnose Churn by Customer Segment
Medium

Investigate why one customer segment drives most churn and what actions to take.

User SegmentsChurnDiagnosis
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Handling Missing Data
Hard

Tests data quality handling and correct treatment of missingness.

Window FunctionsData WranglingCTEs
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Model Performance Metrics
Easy

Tests your understanding of evaluation metrics and how you choose them for ML success criteria.

PrecisionAccuracyRecall
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