I started by talking about re-checking the work, which felt obvious and a bit defensive in retrospect.
Emphasize a data-driven, collaborative approach: first validate your analysis rigorously, then seek to understand the team's perspective, and finally facilitate a resolution by aligning on shared goals and evidence. Show that you value team harmony but also integrity in decision-making, using Amazon's Leadership Principles like 'Have Backbone; Disagree and Commit' and 'Customer Obsession'.
Pro tip: Frame the disagreement as an opportunity to strengthen the team's decision-making by uncovering hidden assumptions or data gaps, rather than as a conflict to win. This demonstrates maturity and a focus on collective success.
Double-check your data, methodology, and assumptions to ensure your result is robust. Seek peer review or run additional tests to rule out errors.
Listen actively to the team's reasoning and evidence. Ask clarifying questions to uncover their assumptions and data sources, showing respect for their viewpoint.
Present your findings clearly, focusing on data and logic, not personal opinion. Highlight areas of agreement and propose a joint deep-dive to reconcile differences.
If alignment isn't reached, suggest a small-scale experiment or A/B test to validate the result. If necessary, escalate to a manager with a balanced view, emphasizing the shared goal.
Once a decision is made, commit fully to the team's direction, even if it differs from your initial finding. Document learnings and maintain a collaborative relationship.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.