o_safadinho

It doesn't even have to be regulated per se. For example, Economists aren't regulated the way that actuaries are, but they still have widely agreed upon format and process for how to get people from grad school to employment. It doesn't matter whether you want to work in the Econ department at Harvard, The World Bank or a consulting company like Deloitte.

I'm not sure about Mathematics, but for Statistics the American Statistical Association hold the 'Joint Statistical Meeting' every year. It is part continuing education classes, research presentations and career fair. Every year there are dozens of companies, universities and national research labs interviewing there. They also do a regular salary survey so you know what types of salaries are typical for Masters/PhD holders in different parts of the country and different industries.

If you plan working in predictive modeling at an insurance company you will have to get credentialed by one of the two actuarial societies. The SOA just added more exams to their current schedule to cover various predictive modeling topics and the CAS decided to make the predictive modeling portion a separate credential. But this way, you do everything once and it is valid across most English speaking countries. It doesn't matter whether you want to work at AllState, ESurance or Liberty Mutual. They just pull your record with one of the Actuarial societies.

I was saying that depending on what you specialize in they do have a lot in common. Given that you have similar specialties, why are economists and actuaries and statisticians able to judge a persons competence in some sort of standardized way but a tech company trying to hire someone in Machine Learning or Data Mining isn't able to do the same?

You obviously wouldn't take an actuarial exam if you don't plan on working in Insurance. However, if you do plan doing something like predictive modeling for an insurance company then it would have to do it.

Both the SOA and the CAS recently changed their exam process to include things like various predictive modeling and clustering techniques. The SOA decided to extend the number of exams and the CAS decided to make it a separate certification.

I read the article correctly. They don't call it homework in the article and instead decide to use the term "data challenge". You can call it whatever you want it is the same thing. It is also split in to four parts and takes place over several days. How did I interpret this incorrectly?

I'm specifically talking about other technical fields that are highly mathematical and that have a lot in common with Computer Science.The fields that I specifically mentioned were Statistics, Economics, Actuarial Science and Operations Research. I mentioned all of the fields that I'm talking about in the original post and in sever of my replies. You are harping on something trying to correct a point that I'm not even trying to make and your response actually helps to further prove my point. You could be correct about the Chemistry thing. I didn't study Chemistry and I don't want to be a Chemist and I didn't mention the subject anywhere in my OP, so I don't really care about the interview process for hiring a Chemist.

For example, with Economics (one of the fields that I actually mentioned), presenting your "Job Paper" is actually part of the interview process. You make your presentation and everybody that has arranged an interview with you can come by and hear your talk. They can read your paper before hand so they have time to think questions well ahead of time. The American Economics Association provides one platform for all interested parties to come together and hash things out instead of me having to try and go to 10 separate interviews.

With Actuaries (another field that I actually mentioned) there is no need for you to quiz me to see if I actually know how a Poisson Process o GLMs work because that is taken care of by the exam process. If somebody passed the Statistical Models exam then you know that they at least meet the industry agreed upon minimum competency level for knowledge of specific Statistical Models.

For one thing, there is a reason that "The Professions" were standardized. At one point in history, there was way less regulation for a lot of professions. But then people had problems with insurance companies becoming insolvent and not actually being able to pay all claims when major disasters struck. This was because companies were hiring people that really didn't know how to do the math.

It also seems like it is also more common for actuaries, statisticians, Industrial Engineers to do independent consulting where they are employed as an independent professional and will be required to carry things like insurance. In cases like that, there really isn't any place that acts as "your employer". Instead, you are independently providing your services to a client.

All of the above do have various sub-specialties. The problem is that even the CS specialties (A.I./Machine Learning/Data Mining) still don't really conform with the other fields that I mentioned. Generalized Linear Modeling is Generalized Linear Modeling; it really doesn't matter whether you are classifying risks for an insurance company or classifying text documents at some tech start up. The math works the same.

In addition to that all of the other fields that I mentioned have sub-specialties as well. An actuary can be a life, health or P&C actuary. They can work in reinsurance or predictive modeling. An economist can work in Financial Economics or Labor markets or Econometrics/Quatitative Methods.

Yet the SOA/CAS are able to come up with and evaluate a set of minimum competencies that insurance companies in most English speaking countries are able to agree on. The American Economics Association is able to bring together major international banks (Various branches of the Fed, The World Bank, Various National Banks, Consulting Companies, Law firms that specialize in Financial/Economics issues) and grad students and it gives them a common format to get to know each other and evaluate job offers regardless of a candidate's/company's specialty.

How does that explain INFORMS for Operations Research?

WWII was the catalyst for a lot of technical research. It spawned the birth of the modern computer with Turing and it also saw the birth or what is now considered modern Operations Research. For example, during WWII George Dantzig (the person that developed the Simplex Algorithm) served in the U.S. Air Force Office of Statistical Control. He only returned to finish his PhD at Berkeley after the was was over. Also a lot of work with Bayesian search started developing around this time to do things like help search for enemy subs.

However INFORMS is very involved in the OR/MS community and multiple times per year they host meetings where one of the goals is to connect grad students with companies.