Want to be a data scientist in a large org. with limited data science sophistication? You may not need an advanced mathematics background to make an impact; out-of-the-box models/techniques might produce actionable results if you have access to clean data.
Try doing the same thing at sophisticated tech companies (FANG, BAT, etc.). The datasets are massive, heterogenous, and often unstructured. Add to that, large teams full of former Ph.Ds and clever engineers have already given the common problems a go, so any better solution will need to be 'more clever'. It's difficult to be 'more clever' without advanced math.
What is hilarious about these articles is none of them ever say "Math as a SENIOR data scientist", because we all acknowledge that senior scientists know the math. Almost every job listing for senior data scientist will prefer a Ph.D. On larger teams, the Ph.D (advanced math) crowd will come up with the general approach that junior data scientists will work on. So yes, getting in the door as a data scientist requires having enough math/programming so you can at least understand what a senior scientist wants you to do. But if you want to lead a team in a technical capacity, you can't possibly imagine doing that without really knowing the math or having a brilliant track record.
And if you are the rockstar 10x guru Data Scientist who consistently delivers actionable insights without a strong math background, you deserve the respect of everyone here and will probably be my boss someday.
In other words, being a Data Scientist requires performing "scientific" activities. Most of these articles focuses on the "Data" part and very rarely about "science". Science requires excellent scholarship - synthesis of current situation and putting future proposals in context of the current "jigsaw puzzle". Now, that is a skill that you can not learn by reading - you need to practice it for someone period of time. No wonder, Ph.D is a requirement for such positions.