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?

Does that really suprise you?

During your PhD you usually learn how to conduct research in academia, which qualifies you for a position in academia but not particularly for industry. Sometimes you are lucky and your topic is also interesting for companies but overall only a small percentage of academia topics are interesting for companies.

Some people now argue that a good researcher can adapt to topics in industry and that you learned tons of other useful skills but the truth is that we have a massive oversupply of scientist that neither academia, nor companies need to that extent. All gained the same soft skills, all published, all hold a Phd....There is no need for that potential. Who needs thousands of neuro scientists? What shall they do in industry? In the average company you have a few people with PhDs who lead teams of Non-PhDs. There are exceptions but most of the time it is like that. As we all know, academia has no positions for these people either.

The solution to this problem is pretty simple and gets also support from a lot of professors (there were even nature articles about the issue) :

Stop training so many PhD students !

If we have no use for so many scientists, the solution cannot be that we just create positions so no potential is wasted. The problem is simply addressed in the wrong way.

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?

Well, there are of course still promising fields like computer science, where know how is needed. Big Data is a hot topic at the moment and I heard for instance that many physicists try to get in there after their PhD in less applied fields like particle- or astrophysics. However, this is not representative and does not really show what the average PhD student can do.
The more programming you do, the better. People in bio-, neuro-, whatever-informatics usually find something appropriate. Significantly harder if you hold a PhD in humanities, protein biochemistry or physical chemistry) ;)