I fumbled this a bit because I defaulted to a story where I actually did have some data, just not a lot.
Use the STAR method to tell a concise story about a product decision you made with incomplete data. Highlight how you framed the decision, leveraged available signals, and mitigated risks. Emphasize the outcome and what you learned about making decisions under ambiguity.
Pro tip: Show that you can distinguish between reversible and irreversible decisions (Type 1 vs. Type 2) and that you're comfortable making reversible decisions quickly with less data. This demonstrates Amazon's bias for action and comfort with ambiguity.
Briefly describe the product, the decision at hand, and why customer data was insufficient. Explain the stakes and the constraints you faced.
Detail how you gathered whatever data was available (e.g., qualitative insights, market trends, internal metrics) and how you involved stakeholders. Describe the frameworks or principles you used to make the call.
Explain what decision you made and how you communicated it. Highlight any steps you took to mitigate risk, such as running a small experiment or setting up guardrails.
Quantify the results if possible, including any learnings or adjustments made post-decision. Be honest about what worked and what didn't.
Summarize what you learned about making decisions with incomplete data and how you've applied that learning since. Tie it back to Amazon's leadership principles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.