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After finishing my PhD in Computer Science (machine learning) and after several years as a university lecturer, I decided to transition from academia to industry. As a result of that, I have been working one year for a company, doing applied research.

In my latest performance review, my supervisor pointed out that my only negative point was that I still had an academic mindset towards research, and that I should be able to get better at what he called "risk analysis". He defined risk analysis as the skill of assessing in advance the potential benefits/drawbacks for the company of a given method/technique/algorithm. The aim of that is to be able to rapidly discard methods that are supposed to not solve the company's problems without having to waste too much time on implementing them.

I was wondering whether any learning resource exists (book, online course/resources) that may help me to acquire such a skill. More generally, any recommended reading about computer science research in industry would be much appreciated.

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    This question appears to be off-topic because it not about academia; it is seeking resources about a specific skill for industry. – EnergyNumbers Jul 8 '14 at 13:57
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    @EnergyNumbers This site is for academics as well as anyone in or interested in research-related or research-adjacent fields which includes industry research. You can debate whether "risk analysis" is really a research skill or not, but the fact that it takes place in industry research and not academia does not make it off-topic. – ff524 Jul 8 '14 at 14:00
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    When I was in engineering industry years ago, non-PhD managers often had this opinion of engineers with PhDs. Does your supervisor have a PhD? If so, you could ask them how they developed this skill. If not, then your manager is just "pointing out the obvious." Don't sweat it; you'll develop this skill over time. – Mad Jack Jul 8 '14 at 15:30
  • @MadJack - my supervisor does not have a PhD. He has a very business-oriented technical background, and his field of expertise is not machine learning, but programming and electrical engineering. – Pablo Suau Jul 8 '14 at 15:53
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    @ff524: I'd argue that "risk analysis", as defined here, is also used in academia - in "real" research, you also need to assess as early as possible whether a given approach/question/whatever will be fruitful, beneficial, whatever... or impractical, useless, whatever else. So +1 to your comment. – Stephan Kolassa Jul 9 '14 at 6:49
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Here are some links for you

this blog

books from Amazon

more books from Amazon

I am not interested in risk assessment but if I was, would sort the books by customer review, would see what the people who read them think, if possible would look inside the book.

Have to say though that I find strange you didn't do it yourself before posting the question.

  • Thank you for your answer. I, indeed, did several searches on Google, Amazon, etc. prior to asking the question. The issue with that approach is that the amount of published information is overwhelming, and therefore I was expecting somebody to point me in a more right direction. – Pablo Suau Jul 16 '14 at 10:42

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