Sure, look at their personal projects. I’m just saying the maintainability and quality of the code and speed of iteration is more of the point than how impressive the math is behind an ML algorithm. I’ve just seen a lot of ML engineers/data scientists who really suck at writing maintainable code
And how would you demonstrate clean code and check for maintainability or patterns? How can you gauge the value of their trial and error?
Look at their code, look at their work. It is a point of reference for potential and actual scenarios.
This would absolutely increase their odds.
Sure, look at their personal projects. I’m just saying the maintainability and quality of the code and speed of iteration is more of the point than how impressive the math is behind an ML algorithm. I’ve just seen a lot of ML engineers/data scientists who really suck at writing maintainable code