Scientific computing is a major use case. Just as a data point, last month I needed a small library to test a signal processing approach and tried to write that

Scientific computing is a major use case. Just as a data point, last month I needed a small library to test a signal processing approach and tried to write that in Go (a good, real problem to better learn Go). I did not get far before I gave up and re-did it in C++. Julia or numpy would work just as well (I just did it quicker in C++). What killed me with Go was lack of generics (a lot of copied code to implement exactly the same integration approach for real and complex functions; different accuracies, etc.); clumsy way to express formulas (no operator overloading to provide a clean syntax for matrix operations) was a pain as well. I have seen many times that the use cases of scientific computing would benefit from the functional programming paradigms, efficiency with basic math operations and ability to define operations that match standard scientific notation (matrix algebra, convolutions, etc.), but there are few languages that do well in all of those.