Now, I work with a lot of people way smarter than I am, who are mostly useless because they can hardly prototype their stuff in Python or run an SQL query. And they'd expect to only work on the best, cleaned and formatted dataset and only do high-end maths on those. Reality hits hard, we're losing money paying them and they're wasting their time not doing what they like. Add to that the frustration / jealousy that this creates.

In that regard, I like that famous definition for Data Scientist: "A programmer that know more about statistics than most programmers, or a statistician that knows more about programming than most statisticians".

And don't get me started on the general repulsion for understanding the basics of how a business runs from academia. Data Science is all about application.

So you want employees who can understand complex mathematics and science but are also good software engineers.

Those people exist, but you have to pay to get them.

(As an aside, lots of Ph.D.'s -- especially in CS -- build systems that are as good or better than a lot of industry code. All of the best and worst code I've read has come from Academia.)

His original Code was cli program who's sole prompt was ?

You had to enter integers , 1, 2 3 etc to select the next option - the possibilities for errors where immense - by this time we had scaled to 1:1 tests where the chemicals for a run could cots over 10K£ per run