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Updated weekly · Last refresh Aug 30

Sprinklr Data Scientist Interview Questions

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

24questions
~3htotal time
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1
MetricsStart here. 3 questions · ~25 min
Choose Metrics for Feature SuccessMedium

Define a metric framework for evaluating a new feature, from immediate adoption signals to long-term retention impact.

Funnel AnalysisKPIsLeading IndicatorsSprinklr
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2
Machine Learning9 questions · ~74 min
Handling Imbalanced Classification DataMedium

Explain how to train and evaluate a classifier when the positive class is rare and accuracy is misleading.

Hyperparameter TuningCross-ValidationFeature EngineeringSprinklr
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3
Behavioral & Leadership4 questions · ~33 min
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4
More topics8 questions · ~65 min
Design Test for New FeatureMedium

Design an A/B test for a new platform feature, including success metrics, power, guardrails, and a clear ship decision.

experiment designfeature evaluationA/B TestingSprinklr
Diagnosing a Product Metric DropMedium

Decide whether a metric drop reflects a real shift or normal variation using hypothesis testing, confidence intervals, and baseline variability.

Confidence IntervalsHypothesis TestingStatistical SignificanceSprinklr
SQL Window Functions for EngagementMedium

Tests your ability to use SQL window functions to derive engagement insights from customer data.

Window FunctionssqlSprinklr
LLM Training and Generative AI UsesMedium

Assesses your understanding of LLM training and practical generative AI applications.

generative aiSprinklr
Optimizing Search for Data StreamsMedium

Assesses your approach to scalable search and streaming performance constraints.

optimizationSprinklr
Generative AI in Unified-CXMMedium

Evaluates product sense for applying generative AI to customer experience workflows.

generative aiSprinklr
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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
Find Largest Funnel Step Drop-OffHard
Practice
Practice drill

Use joins, aggregations, and a window function to find the funnel step with the largest user drop-off.

Window FunctionsFunnel AnalysisCase WhenAsanaAisCanva
Analyze Asana Onboarding Funnel DropoffHard
Practice
Practice drill

Use CTEs, joins, and conditional aggregation to measure 7-day onboarding funnel conversion and identify the biggest dropoff step.

Window FunctionsFunnel AnalysisDate FunctionsRipplingIntuitAsana
Identify Funnel Bottlenecks in OnboardingHard
Practice
Practice drill

Use joins, CTEs, and aggregations to find the weakest step in an onboarding funnel and estimate lost conversions.

Funnel AnalysissqlDiagnosisAtlassianRevolut
The finish line: interview-readyComplete all 24 questions plus 3 hands-on drills to finish this plan.