Analysis to Recommendation and Decision Framing Questions
Ability to move from analysis to a concise, justified recommendation and a pragmatic plan for decision and implementation. Candidates should lead with a clear recommendation or conditional decision, support it with evidence and trade offs, quantify expected business impact, estimate effort and time horizon, and state assumptions and limitations. The skill set includes proposing prioritized action plans and alternative options, anticipating objections, defining monitoring and rollback strategies, translating technical remediation or risk into business terms and measurable success metrics, and tailoring recommendations to stakeholder needs and constraints.
HardTechnical
58 practiced
A new signup flow launched on a specific date. Describe the Interrupted Time Series (ITS) model you'd use to estimate the effect on weekly signups while adjusting for trend and seasonality, including model specification (autoregressive terms, seasonal dummies), diagnostics (autocorrelation, Durbin-Watson), and how you'd implement it in Python (libraries and key steps). Finally, explain how you'd convert the effect into a yearly revenue impact.
EasyTechnical
78 practiced
Explain the difference between absolute change, relative change (percent), and lift in the context of business metrics. Provide guidance on when to lead with each metric when making recommendations to stakeholders (e.g., product manager vs. CFO), and describe one visualization you would use to make each clear in a dashboard.
HardTechnical
63 practiced
Propose a cross-functional governance process to unify metric definitions (e.g., 'active user', 'monthly revenue') across product, analytics, and finance. Detail roles and responsibilities, versioning and change control, a data catalog approach, SLA for changes, migration strategy for dashboards, and KPIs to measure success of governance. Explain trade-offs between centralization and local autonomy.
MediumTechnical
60 practiced
You have six analytics backlog requests. Propose a prioritization framework (scoring rubric) that balances business impact, implementation effort, data risk, and strategic alignment. Apply your rubric to these example requests and justify the top two picks: 1) migration of core dashboards to new BI tool, 2) predictive churn model, 3) marketing attribution overhaul, 4) reconciliation report for payment failures, 5) instrument new checkout event, 6) A/B testing platform integration.
HardTechnical
74 practiced
A stakeholder asks: how long will an A/B test take to detect a 1% relative lift in conversion if baseline conversion is 3%, with 80% power and alpha=0.05? You have 1,000,000 weekly visitors randomized evenly. Show the sample size calculation (or formula), compute necessary sample per arm and calendar duration, state assumptions, and explain how variance or clustering might change the answer.
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