Top 13
Prep plan
Updated weekly · Last refresh Oct 6
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Ivalua Data Scientist Interview Questions

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

13questions
~2htotal time
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1
SQL & Data ManipulationStart here. 3 questions + 1 drill · ~40 min
SQL Rolling Purchase AverageMedium
Practice

Calculate each customer's 7-day rolling average purchase amount using a date-based window frame.

Window FunctionsDate FunctionsData ManipulationIIvalua
SQL for User RetentionMedium
Practice

Measure monthly user retention by signup cohort using date calculations and aggregated activity.

Window FunctionsDate FunctionsCTEsIIvalua
Optimize Large PostgreSQL Query PerformanceMedium

Explain how to tune slow PostgreSQL queries on multi-million-row tables using indexes, execution plans, joins, and partitioning.

Performance Tuningquery optimizationsqlIIvalua
7-Day Rolling User Average in SQLMedium
Practice
Practice drill

Calculate each user's seven-day rolling average of daily events using PostgreSQL window functions.

Window FunctionsDate FunctionsData ManipulationBounteousFFreelancerBBoston Tech India
2
A/B Testing & Experimentation3 questions · ~30 min
Conflicting Metric ImpactsMedium

Explain how to decide when a treatment improves one metric but harms another, using primary metrics, guardrails, and practical significance.

experiment designTrade-offsGuardrail MetricsIIvalua
Compute A/B Test Sample SizeHard

Determine the sample size needed to detect a meaningful A/B test effect at a chosen significance level and power.

experiment designMDEStatistical SignificanceIIvalua
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3
Behavioral & Leadership4 questions · ~39 min
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4
More topics3 questions · ~30 min
Test New Feature Engagement ImpactMedium

Design an experiment to determine whether a new feature meaningfully improves user engagement without harming core product health.

ExperimentationFeature PrioritizationUser NeedsIIvalua
Diagnose Engagement DropHard

Identify the causes of a quarterly engagement decline through metric validation, decomposition, segmentation, and trend analysis.

MetricsDiagnosisuser engagementIIvalua
Balance Conversion and RetentionHard

Framework for deciding when to favor short-term conversion gains versus long-term retention in a product decision.

Feature PrioritizationUser NeedsValue PropositionIIvalua
The finish line: interview-readyComplete all 13 questions plus 1 hands-on drill to finish this plan.