Clarify the problem and goals first, then break it down into a structured framework (e.g., metrics, drivers, root causes). Walk through your reasoning step-by-step, prioritizing hypotheses and validating with data or logic, while staying adaptable to new information.
Pro tip: Explicitly state your assumptions and how you would validate them; this shows structured thinking and maturity in handling ambiguity, which is highly valued at Google.
Ask questions to understand the scope, goals, and constraints. Confirm what success looks like and what data is available.
Decompose the problem into logical components or drivers (e.g., using a metric tree or issue tree). Identify the key factors that influence the outcome.
Based on impact and likelihood, prioritize which branches to investigate first. Formulate specific hypotheses to test.
Use data, logic, or experiments to test hypotheses. Quantify where possible and identify root causes.
Synthesize findings into actionable recommendations. Consider trade-offs and next steps, and be open to revising your approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.