I'm one of the developers of Weld -- Numba is indeed very cool and is a great way to compile numerical Python code. Weld performs some additional optimizations specific to data science that Numba doesn't really target right now (e.g., fusing parallel loops across independently written functions, parallelizing hash table operations, etc.). We're also working on adding the ability to call Python functions from within Weld, which will allow a data science program expressed in Weld to call out to other optimized functions (e.g., ones compiled by Numba). We additionally have a system called split annotations under development that can schedule chains of such optimized functions in a more efficient way without an IR, by keeping datasets processed by successive function calls in the CPU caches (check it out here: https://github.com/weld-project/split-annotations).

Overall, we think that the accelerating the kinds of data science apps Weld and Numba target will not only involve tricks such as compilation that make user-defined code faster, but also systems that can just schedule and call code that people have already hand-optimized in a more efficient and transparent way (e.g., by pipelining data).