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Tiger Analytics Data Scientist Interview Questions

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

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1
Machine LearningStart here. 19 questions · ~158 min
Model Carryover Effects of TV Advertising in MMMMedium

Develop a regression model to quantify the carryover effects of TV advertising on sales over time in a marketing mix model.

RegressionCausal InferenceTime SeriesTiger Analytics
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2
Statistics & Probability4 questions · ~33 min
Understanding Type I and Type II Errors in TestingMedium

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

Hypothesis TestingStatistical SignificanceP-ValuesTiger Analytics
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3
Model Evaluation6 questions · ~50 min
Evaluate F1 Score Significance in Model PerformanceMedium

Analyze the significance of the F1 score in a binary classification model for customer churn prediction, and propose improvements.

F1 ScoreAccuracyTiger Analytics
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4
Metrics9 questions · ~75 min
Evaluate Feature Success Metrics for New App UpdateMedium

Identify key metrics to assess the success of a new feature in a mobile app update and propose a metric evaluation strategy.

KPIsEngagement MetricsTiger Analytics
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5
SQL & Data Manipulation3 questions + 3 drills · ~55 min
Self-Join Manager Salary CheckEasy
Practice
Practice drill

Use a self-join to identify Tiger Analytics employees whose salaries exceed their direct managers.

SubqueriesJoinsSelf-JoinsNevada StaffingTiger Analytics
Analyze Customer Purchase Trends with Window FunctionsEasy
Practice
Practice drill

Calculate the monthly spending trends for customers using window functions and joins.

The Home DepotAvisoWorld Wide Technology
Retention Rate SQL With JoinsMedium
Practice
Practice drill

Calculate consecutive-month customer retention rates using joins, aggregation, and date-based CTEs.

aggregationsql queryTiger Analytics
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6
More topics9 questions · ~75 min
Compare Zero-Shot Prompting vs Fine-Tuning in NLPHard

Analyze trade-offs between zero-shot prompting with GPT-4 and fine-tuning LLaMA-3 for text classification tasks.

Language ModelsDeep LearningTiger Analytics
Design a Personalized Product RecommenderHard

Design an end-to-end product recommendation system for a large e-commerce marketplace with strict latency and freshness needs.

Feature StoreFeature DriftModel ServingTiger Analytics
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityTiger Analytics
Design Analytics Dashboard for Nontechnical UsersMedium

Design an analytics dashboard that helps nontechnical users understand performance and take action without getting lost in complexity.

User SegmentsUser Needsuser-centric designTiger Analytics
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