Interesting, thank you for reply. I clearly see what you are saying, and yet it's kind of weird when I see articles claim there is a shortage of data scientists on the market right now (see below examples from gartner and mckensey)

Though I see your viewpoint and understand problem you are describing, is there any statistics in hand you can share?

I'd like to get your feedback on how hard was for you to apply your research after graduation.

Some time ago I worked on research for my paper (neural networks for heart attack risk classification for people with cardiovascular diseases). I spent zillion amount of time and energy to get datasets, prepare and fine tune models, prepare paper and then.. Then I had to move on something else, because at that time create real life application required starting up a company, which for academia person was quite a challenge at that time.
Recently as a part of my work on data project, I had discussions with PhD in my network and found out that this was common problem among them. Usually PhD students work really hard to get PhD degree, I mean really hard: 5+ years 12 hours a days 6 day a week, publishing great papers and then some of them just cannot get a cool research / scientist job! 5 out of 7 really cool people, I talked with, ended up on engineering positions, which they could easily get after BA/MS degree.

I personally had same experience before, I see there is definitely wasted potential, and now want to approach this issue. Though even have some thoughts what can be done, before diving into solving anything (my sample could be biased after all) I decided to give benefit of a doubt and would like to get feedback from the community first.

So what was your experience on capitalizing on your models and getting dream position after PhD grads?