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Kotak Mahindra Bank Data Scientist Interview Questions

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

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1
SQL & Data ManipulationStart here. 4 questions + 3 drills · ~63 min
Handling Missing and Dirty SQL DataMedium
Recently asked

Explain how to profile, clean, and standardize missing or dirty data before analysis.

Data WranglingCase WhenQualityKotak Mahindra Bank
Monthly Sales Trends by CategoryMedium
Practice
Practice drill

Aggregate monthly sales by product category and use LAG to calculate month-over-month changes.

InfrastructureToolsData WranglingTotal Wine & MoreInc.Benjamin Moore
Normalize Messy CRM Contact DataHard
Practice
Practice drill

Clean inconsistent CRM contacts by joining source tables, standardizing values, and flagging bad records.

Data WranglingCase WhenQualityAlphaSense
Validate Monthly Revenue Data QualityHard
Practice
Practice drill

Use joins, a CTE, and CASE logic to flag messy monthly order data and produce cleaned revenue by month.

Data WranglingCase WhenQualityLiterati
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2
Statistics & Probability6 questions · ~50 min
Explaining P Values ClearlyEasy
Recently asked

Explain what a p-value means, how it relates to statistical significance, and how to describe it clearly to non-technical stakeholders.

CommunicationStatistical SignificanceP-ValuesKotak Mahindra Bank
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3
A/B Testing & Experimentation3 questions · ~25 min
Common Pitfalls in Experiment ResultsHard
Recently asked

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

PeekingNovelty EffectSample Ratio MismatchKotak Mahindra Bank
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4
Machine Learning8 questions · ~67 min
Bagging vs Boosting ExplainedMedium
Recently asked

Explain how bagging and boosting differ, and identify a representative algorithm for each ensemble method.

Ensemble Methodsmodel trainingSupervised LearningKotak Mahindra Bank
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5
Pipelines3 questions · ~25 min
Data Quality in ETL PipelinesEasy
Recently asked

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityKotak Mahindra Bank
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6
Model Evaluation3 questions · ~25 min
Manage Production Model DriftHard
Recently asked

Approach for detecting, interpreting, and responding to model drift in a production AI system.

CalibrationAUC-ROCThreshold TuningKotak Mahindra Bank
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7
More topics10 questions · ~83 min
First Checks for Metric DropsEasy
Recently asked

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

Lagging IndicatorsLeading IndicatorsDiagnosisKotak Mahindra Bank
Product Metric for EngagementMedium

Tests your ability to define and operationalize a business metric for banking app engagement.

user engagementKotak Mahindra Bank
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The finish line: interview-readyComplete all 37 questions plus 3 hands-on drills to finish this plan.