I blanked for a second because every PM story I prepped was about data saving the day.
Choose a specific instance where you misinterpreted data, leading to a suboptimal decision. Use the STAR method to describe the situation, the data-driven decision, the negative outcome, and how you identified and corrected the mistake. Emphasize the lessons learned and how you improved your data analysis or decision-making process afterward.
Pro tip: Show that you embrace being wrong as a learning opportunity and that you have mechanisms in place to challenge your own assumptions, such as seeking diverse perspectives or running experiments. This demonstrates Amazon's Leadership Principle of 'Learn and Be Curious' and 'Insist on the Highest Standards'.
Briefly describe the project, your role, and the goal. Provide enough background so the interviewer understands the stakes and your responsibilities.
Explain what data you used, how you interpreted it, and the decision you made based on that interpretation. Be specific about the metrics and analysis.
Detail the negative outcome or realization that the data led you astray. Clarify what actually happened versus what you expected, and why the data was misleading.
Describe how you identified the error, what steps you took to mitigate the impact, and how you pivoted to a better solution. Highlight any collaboration or leadership you demonstrated.
Summarize what you learned from the experience and how you changed your approach to data analysis or decision-making to prevent similar mistakes in the future.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.