[aqsalose]

To piggyback on the OPs question, I for one think the part in parenthesis is actually most important:

>(Data cleaning and management should also be learned)

There are many students and graduates who either didn't want to do research in the first place or didn't get that research grant or position and looking to get employed in private sector with their degree. Many universities and colleges have now also retooled some of their statistics degrees as dedicated "data science" curriculum who either know basics of ML/DL or have the prerequisite background to learn quickly.

However, in my experience (I am extrapolating from my own past job search experiences) while "understanding theory behind the algorithms" counts still for something, it is much less than one would think. Familiarity with the software technologies and practical implementation is what counts much more. This includes not only "data management", a phrase which makes it sound like the data simply exists somewhere and only needs to be managed (not unlike a Kaggle competition), but also the data pipeline management from generation/collection to analysis and communication of the results, and deploying the software the implements it all, and so on. I suppose (never been on that end of the interview table) given any two candidates to interview, it is very difficult to evaluate how deeply one understands theory of some algorithm compared to other if they both demonstrate some basic understanding (and what is the practical use of possible difference in insight from such differential, anyway?). Likewise, I assume it is somewhat easier to gauge whether someone seems to able start delivering results or contributing to their on-going work quickly if they have the relevant technical skills and/or domain knowledge.