However, I have to acknowledge that modern IDEs (and even advanced code editors like VSCode) have pretty good support for dynamic scripting languages these days that make working with them in larger code bases not entirely painful.

These were the go-to "prototyping" languages people in science, R&D, etc. used (and still use!), and then you'd port those into more manageable languages for larger scale projects, or actual software products.

These days, Python does all that. Unless you need some extreme performance for your final product, a well-optimized Python program will work just fine.

But still, there's some of that matlab legacy left - instead of one comprehensive application, you have tons of smaller programs floating around, for one-off/single-issue uses.

I've found that using Python for large projects is both fine and productive as long as you utilize type annotations and modules. Ambiguity goes out the window when you do that.

In the end I feel, if you really want that, use a language that treats types as a first class citizen. There are languages with most of Python's attractive features but actual optional typing (we use Groovy), or static with really good inferred types (eg: Kotlin) etc.

Besides VS Code, these days lightweight editors like Kate, Sublime Text, Vim, etc can all take advantage of type checking LSP servers for Python.

[1] https://github.com/palantir/python-language-server

[2] https://github.com/tomv564/pyls-mypy

It's quite good.