I went with analogies for the first part, talked about explaining a model's confidence interval like a weather forecast range rather than dumping stats on them.
Structure your answer around the core principle of audience-centric communication: adapt your language, analogies, and level of detail to the listener's background and goals. Use a concrete example for each direction to demonstrate empathy and clarity, and emphasize checking for understanding through feedback loops.
Pro tip: At Stripe, where engineers frequently interface with finance, product, and sales teams, show you understand that translation is bidirectional—you must also actively learn the other domain's vocabulary. Mention that you often ask stakeholders to explain their mental model first, so you can anchor your explanation to what they already know.
Before explaining, clarify what the stakeholder needs to decide or do with the information, and gauge their existing knowledge. This ensures you focus on relevance rather than completeness.
For non-technical stakeholders, use everyday analogies (e.g., comparing APIs to restaurant menus) and avoid jargon. For technical audiences, ground business concepts in metrics, systems, or code-like logic they already understand.
Start with a one-sentence summary, then add layers only as needed. This respects the listener's time and allows them to stop you when they have enough context.
Ask open-ended questions like 'How does that land?' or 'What part would you like me to go deeper on?' to confirm comprehension and adjust in real time.
Connect the explanation to business outcomes (e.g., revenue, risk, speed) or technical implications (e.g., scalability, maintainability) so the listener knows why it matters and what to do next.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.