What\u2019s the point of testing machine learning model knowledge during interviews for non-research data science roles?
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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.<\/p>\n
However, someone recently asked me a question that I hadn\u2019t given much thought to before: what\u2019s the point of testing machine learning model knowledge during interviews for non-research data science roles?<\/p>\n
Interview questions about model knowledge often include the following, especially if a candidate claims to have experience with these models:-<\/p>\n
I learned these concepts because I\u2019m 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\u2019t in a research position and primarily uses these tools to solve business problems?<\/p>\n
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\u2019t come up with a convincing argument to justify why it is fair to test this knowledge, other than \u201cthe candidate should know it if they are using it.\u201d<\/p>\n
What\u2019s your experience with this topic? Do you think understanding the inner workings of machine learning models is critical enough to be tested during interviews?<\/p>\n<\/p><\/div>\n
submitted by \/u\/orz-_-orz <\/a>
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