Acknowledge the feedback without defensiveness, then reframe it as an opportunity to bridge technical and business perspectives. Provide a concrete example where you translated ML work into business impact, and describe how you actively seek business context to improve your decisions.
Pro tip: Show that you already partner with product or business teams to define success metrics, and mention a specific instance where your business acumen led to a better technical decision.
Thank the person for the feedback and acknowledge that business sense is critical for ML engineers. Avoid sounding defensive or dismissive.
Ask for specific examples to understand the gap, showing you're open to learning. This also buys time to formulate a thoughtful response.
Provide a concrete example where you used business understanding to guide an ML project, such as optimizing for a business metric rather than a technical one.
Outline steps you're taking to improve, like taking business courses, shadowing product managers, or regularly reading industry reports.
Emphasize that your goal is to deliver business value through ML, and you're eager to close any gaps to achieve that.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.