Tell me about a time when you had to lead a team through a major architectural decision for an ML model, and how you resolved conflicts or differing opinions among stakeholders to reach a consensus.

Use STAR framework: 1) Situation (context of the decision), 2) Task (your role/leadership responsibility), 3) Action (how you facilitated discussion, resolved conflicts, made the decision), 4) Result (measurable outcome of the decision). Focus on demonstrating leadership, technical judgment, and conflict resolution skills.

During a project to deploy a real-time fraud detection system, our team faced a critical architectural decision between using a deep learning model for accuracy or a lightweight ensemble model for latency. As lead AI/ML engineer, I organized cross-functional workshops with stakeholders including product managers, data scientists, and engineering leads. Conflicts arose between teams prioritizing accuracy vs. scalability. I facilitated a structured evaluation by creating a decision matrix comparing model performance, inference speed, and maintenance costs. We ran A/B tests with production data, revealing the ensemble model achieved 92% accuracy with 30% lower latency. By presenting this data objectively and aligning the decision with business KPIs, we reached consensus. The chosen architecture reduced fraud detection latency by 40% while maintaining 91% accuracy, improving user experience and reducing false positives by 25%.