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Stripe Interview Problems (47)

Problems reported from Stripe data science interviews

Status	Title	Difficulty	Language	Company	Topic
	Regression Homoscedasticity Pro	Medium	Conceptual	Capital One JPMorgan Stripe	statistics
	Running Revenue by Salesperson Pro	Medium	SQL	Salesforce Stripe LinkedIn	window-functions
	Top N Per Group with ROW_NUMBER Pro	Medium	SQL	Amazon Airbnb Stripe	window-functions
	Total Spend Per Customer Pro	Medium	SQL	Amazon Stripe Shopify	joins
	Unmatched Records: Orders Without Customers Pro	Medium	SQL	Stripe Airbnb Uber	joins
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	Amortized Analysis Pro	Hard	Conceptual	Google Stripe	computer-science
	AUC-ROC Interpretation Pro	Hard	Conceptual	Stripe PayPal Capital One	machine-learning
	Cumulative Sum Window Pro	Hard	SQL	Airbnb Stripe Two Sigma	window-functions
	Dealing with Data Quality Issues Pro	Hard	Conceptual	Stripe Amazon	culture-fit
	Distributed Computing Challenges Pro	Hard	Conceptual	Google Amazon Stripe	big-data
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	Handling Class Imbalance with SMOTE Pro	Hard	Conceptual	Capital One Stripe JPMorgan	machine-learning
	Join with Aggregation and Filtering Pro	Hard	SQL	LinkedIn Uber Stripe	joins
	Latest Order Per Customer Using Join and Subquery Pro	Hard	SQL	Amazon Stripe Airbnb	joins
	NTILE for Quartile Bucketing Pro	Hard	SQL	Stripe Airbnb Two Sigma	window-functions
	Orders Exceeding Customer Lifetime Average Pro	Hard	SQL	Amazon Stripe Shopify	subqueries
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	Percentile Rank of Order Values Pro	Hard	SQL	Stripe Google Meta	window-functions
	REST API Design Principles Pro	Hard	Conceptual	Stripe Airbnb	computer-science
	Revenue Contribution by Region With Rollup Pro	Hard	SQL	Walmart Amazon Stripe	group-by
	ROI Calculation for a Data Project Pro	Hard	Conceptual	Stripe Coinbase Robinhood	business-skills
	Running Total Pro	Hard	SQL	Amazon Stripe	window-functions
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	Running Total of Order Amounts Per Customer Pro	Hard	SQL	Stripe Airbnb Netflix	joins
	Target Encoding Pro	Hard	Python	Stripe Capital One PayPal	feature-engineering

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