>in data analysis/science, there's more than way to highlight/analyze a problem, and more than one way to solve it. In my experience, the best solutions are oft

>in data analysis/science, there's more than way to highlight/analyze a problem, and more than one way to solve it. In my experience, the best solutions are often the craziest. I strongly disagree. I have hired, mentored, and/or managed 20+ data scientists and analysts over my career; in my experience the ones who come up with "crazy"/creative solutions are often the least effective in the long run. This is especially true on the product analytics side, where there are 5 ways to shoot yourself in the foot in non-obvious and subtle ways. The best analysts I've worked with follow fairly regimented best practices around the way they analyze problems and present solutions. By its very nature, non-standard solutions are more error-prone and harder to explain, which breaks institutional trust in the analyst. That said, there is plenty of room for analysts to grow into more creative fields like machine learning or product management, but I don't think the person being interviewed falls in one of these categories.