Importance of Underlying mathematics behind AdaBoost, Gradient Boost and other Machine learning techniques

I am trying to understand how important is it to know the mathematical formula under the hood of these techniques, from interview and job(or practical application) point of view. For example I know how they work, the steps behind everything and how it is computed, like I have proper understanding of Information Gain, Entropy, Gini Index when it comes to decision trees, but I don’t know the formula for these. How crucial are they keeping in mind that these are always calculated for us?