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Updated weekly · Last refresh Oct 27

Retina AI Data Scientist Interview Questions

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

21questions
~3htotal time
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1
Machine LearningStart here. 3 questions · ~26 min
Handle Highly Imbalanced ClassesMedium

Build a classifier for a highly imbalanced dataset and choose training and evaluation methods that surface rare positives.

Cross-ValidationFeature EngineeringSupervised LearningRetina AI
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2
Statistics & Probability4 questions · ~35 min
Evaluating Observed Lift SignificanceMedium

Explain how to test whether an observed experiment lift is real using hypothesis testing, p-values, and confidence intervals.

Confidence IntervalsStatistical SignificanceP-ValuesRetina AI
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3
Behavioral & Leadership7 questions · ~61 min
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4
More topics7 questions · ~61 min
Diagnose Sample Ratio Mismatch TestMedium

Design an onboarding A/B test with explicit SRM detection, power analysis, guardrails, and a decision rule for whether results are valid.

ExperimentationStatistical SignificanceSample Ratio MismatchRetina AI
Diagnose KPI Drop After ReleaseMedium

Diagnose a post-release KPI drop by separating instrumentation issues from real behavior changes and tracing the problem through the metric hierarchy.

KPILeading IndicatorsDiagnosisRetina AI
Retention Product MetricsMedium

Tests your ability to translate retention objectives into measurable product and ML metrics.

MetricsRetentionuser valueRetina AI
SQL Rolling Purchase AverageMedium
Practice

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

Window FunctionsDate FunctionsData ManipulationRetina AI
CLV With Limited HistoryHard

Tests your evaluation strategy for CLV models under data scarcity and uncertainty.

Evaluation Techniquescustomer lifetime valueModel MetricsRetina AI
Measure Marketing Campaign IncrementalityMedium

Design an incrementality test for a new customer marketing campaign with explicit MDE, guardrails, power, and rollout criteria.

ExperimentationHypothesis TestingGuardrail MetricsRetina AI
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Hands-on SQL practiceWrite and run real queries in the editor. 2 drills · ~20 min
Rolling 7-Day Average by RegionMedium
Practice
Practice drill

Calculate each product-region's rolling 7-day average sales using daily aggregation and calendar-aware windowing.

Window FunctionsDate FunctionsData ManipulationBayerCCoca-Cola Beverages Singaporeddm-drogerie markt
SQL Window Functions for Rolling MetricsHard
Practice
Practice drill

Calculate each user's 7-day activity average and rank users within their cohort using PostgreSQL window functions.

Window FunctionsDate FunctionsData ManipulationOwnerTredenceEchostar
The finish line: interview-readyComplete all 21 questions plus 2 hands-on drills to finish this plan.