But without some statistical knowledge, isn’t there a risk of a lack of understanding about the robustness of “what works”?
Statistical knowledge doesn’t remove that risk. The extent to which it even lowers the risk is a question that could be answered empirically.
yeah, agreed - a good understanding of the model's statistical assumptions can often help you make the model more robust and also give you ideas for what types of feature engineering are likely to work.
Despite using similar models, the expertise required for 'doing statistics' (statistical inference) is actually very different from machine learning. Machine learning fits into the 'hacker mentality' well - try stuff out see what works. To do statistical inference effectively, you really do need to spend time learning the theory. They both require deep skills - but the skills are surprisingly different considering it's often the same underlying model.