Data and Business Outcomes Questions

This topic focuses on converting data analysis and insights into actionable business decisions and measurable outcomes. Candidates should demonstrate the ability to translate trends into business implications, choose appropriate key performance indicators, design and interpret experiments, perform cohort or funnel analysis, reason about causality and data quality, and build dashboards or reports that inform stakeholders. Emphasis should be on storytelling with data, framing recommendations in terms of business levers such as revenue, retention, acquisition cost, and operational efficiency, and explaining instrumentation and measurement approaches that make impact measurable.

HardSystem Design

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Design a scalable reporting platform that supports thousands of dashboards with near-real-time data for multiple internal teams. Describe the data architecture (data lake vs data warehouse), choice of pre-aggregation/materialized views, caching, multi-tenancy, access controls, lineage, cost controls, and how you'd monitor performance and SLA adherence.

HardTechnical

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Your analytics show a consistent performance gap across demographic groups in conversion for a loan product. Describe the steps you would take to investigate whether the gap is due to product design, downstream decisioning models, or data issues. Propose remediation steps, fairness metrics to track, and how you would communicate findings to non-technical stakeholders.

MediumTechnical

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You observe two user acquisition channels with different retention curves. Design an analysis plan to compare long-term value across channels while controlling for differences in initial user quality and demographics. Include data requirements, statistical tests or models, and how you would present actionable recommendations.

HardTechnical

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You want to estimate the causal effect of a region-specific price increase rolled out on 2024-06-01. You have daily revenue by region. Describe a difference-in-differences (DiD) regression model you would run, how you would test the parallel trends assumption, and at least three robustness checks you would perform.

MediumTechnical

0 practiced

Given a transactions table:

transactions(tx_id BIGINT, user_id BIGINT, tx_date DATE, revenue DECIMAL)

Write SQL or describe the steps to compute a 12-month customer lifetime value (CLTV) per cohort (cohort by first_tx_month). State assumptions, how you handle churn/returning users, and how you would discount future revenue.

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