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razorpay Data Scientist Interview Questions

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

Bias-Variance in Model SelectionMedium

Explain how the bias-variance tradeoff guides model selection and generalization.

Cross-ValidationBias-Variance TradeoffRegularization
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Bias-Variance Tradeoff in Model Choice
Easy

Explain how the bias-variance tradeoff guides algorithm selection and generalization performance.

Cross-ValidationBias-Variance TradeoffRegularization
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Common Pitfalls in Experiment ResultsHard

Identify the main pitfalls that can distort A/B test interpretation and explain how to guard against them.

PeekingNovelty EffectSample Ratio Mismatch
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Design and Reflect on A/B Test
Medium

Describe an A/B test you ran, what question it answered, how you measured success, and what you learned from the results.

ExperimentationGuardrail MetricsA/B Testing
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First Checks for Metric Drops
Easy

Outline the first checks to diagnose a sudden drop in a core product metric, starting with data quality, scope, and decomposition.

Lagging IndicatorsLeading IndicatorsDiagnosis
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Diagnose a Performance Drop
Hard

Investigate whether a performance decline is seasonal or a real product issue.

Leading IndicatorsDiagnosisTime Series
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Handling Missing Data in Pipelines
Medium

Approach for handling missing data in an ML data pipeline, including validation, imputation, and safe downstream consumption.

InfrastructureETLBatch Processing
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Understanding Type I and Type II Errors in Testing
Medium

Differentiate between Type I and Type II errors in hypothesis testing with a practical example.

Hypothesis TestingStatistical SignificanceP-Values
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