I am shunning SciPy and to a less extent NumPy for the same reason. I have reason to believe the developers are not experts in numerical linear algebra and some of the documentation also do not lend confidence.
Having seen and heard about what Bioconductor had to do to process genetic data, memory is a huge issue. It is even more so with next-generation sequencing data.
I actually agree with you. I much prefer operator overloading than unwieldy chained function calls. But it might be tough to convince the larger Python community to add it since it is only useful to a small part of the community.
The last comment about dtypes is that the size (shape in NumPy) should be part of the type; certainly element-type should be. A 1-D vector of double should not be the same Python type as 3-D array of characters or a 500x600 matrix. This creates havoc in a dynamic language. I once spent two days tracking down a bug caused by "*" multiplying two arrays instead of two matrices when I started using NumPy. Perhaps size is too much for a dynamic language to be part of type, but surely dimension and element-type should be reflected in Python type. It absolutely does not help that the documentation is littered with type-objects and object-objects; I appreciate this is how C implementations are, but for a beginner NumPy users, it is more than a little confusing.
I have a lot of respect for people who designed and built NumPy; for multi-dimensional arrays I don't see a better approach; but too much of influence of C implementation details seep into Python interface, types and operator overloading are only the beginning of the problems. I am having second thought about the suitability of dynamic languages for large scale, high-performance computing. Type annotation could help a lot but I see there is exactly zero interest in that for NumPy.
I just don't see adding a special matrix infix operator to Python happening. It is too specialized, and yet matrix is not special enough that it must have its own operator. Isn't this what operator overloading is for?
I suspect the real problem is NumPy's type system implementation, since data-types are not visible as different Python types.