What’s the point of testing machine learning model knowledge during interviews for non-research data science roles?

I always make an effort to learn how a model works and how it differs from other similar models whenever I encounter a new model. So it felt natural to me that these topics were brought up in interviews.

However, someone recently asked me a question that I hadn’t given much thought to before: what’s the point of testing machine learning model knowledge during interviews for non-research data science roles?

Interview questions about model knowledge often include the following, especially if a candidate claims to have experience with these models:-

• what’s the difference between bagging and boosting?
• whether LightGBM uses leaf-wise splitting or level-wise splitting?
• what’s the underlying assumptions of linear regression?

I learned these concepts because I’m genuinely interested in understanding how models work. But, coming back to the question: How important is it to have deep technical knowledge of machine learning models for someone who isn’t in a research position and primarily uses these tools to solve business problems?

From my experience, knowing how models learn from data has occasionally helped me identify issues during the model training process more quickly. But I couldn’t come up with a convincing argument to justify why it is fair to test this knowledge, other than “the candidate should know it if they are using it.”

What’s your experience with this topic? Do you think understanding the inner workings of machine learning models is critical enough to be tested during interviews?