Data Problem Solving and Business Context Questions
Practical data oriented problem solving that connects business questions to correct, robust analyses. Includes translating business questions into queries and metric definitions, designing SQL or query logic for edge cases, handling data quality issues such as nulls duplicates and inconsistent dates, validating assumptions, and producing metrics like retention and churn. Emphasizes building queries and pipelines that are resilient to real world data issues, thinking through measurement definitions, and linking data findings to business implications and possible next steps.
HardTechnical
0 practiced
Design a multi-touch attribution model with exponential time decay for an e-commerce product. Describe the modeling approach, how to choose decay parameters, how to compute fractional credit per touch, how to validate the model using holdout campaigns, and potential business impacts when shifting media budgets based on this model.
MediumTechnical
0 practiced
A customer satisfaction survey reports very high NPS while behavioral data shows low engagement. How would you investigate potential sample bias in the survey and reconcile these conflicting signals before reporting to leadership?
MediumTechnical
0 practiced
Define a conversion funnel for a trial-to-paid flow with steps: start trial, activate core features, upgrade to paid. For each step specify event names, SQL logic to compute unique user progress, and how to handle edge cases like canceled trials, duplicate events, and refunds when calculating drop-off rates.
HardTechnical
0 practiced
You observe a change that increases time-on-site but decreases conversion rate. Provide a structured analysis plan to determine root cause: list metrics to compute, segments to analyze, and product hypotheses to test to explain this paradox.
MediumTechnical
0 practiced
You must prioritize three experiments next quarter with limited engineering capacity: improve onboarding completion, increase paid conversion, or reduce churn. Describe which metrics and analyses you would use to prioritize these experiments, how to estimate expected impact and effort, and how to surface this to stakeholders.
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