The first post this year is for OR students who plan to put their ideas into practice.
There is a significant ongoing transformation in the landscape of OR practice. At the end of the first ten years of this century, we see that traditional industries where OR has succeeded in the past such as airlines and logistics will continue to use OR methods. However, due to the saturation and the lack of radical breakthrough ideas, the minor incremental returns for spending R&D dollars will continue to discourage management from enhancing the science behind these OR approaches, and they will further outsource such tools to 3rd party vendors. In such a support mode, there is little that differentiates you from your competition.
On the other hand, the application of OR methods to new industries is very exciting, even lucrative. This year, OR will quietly make its way into more new industries. Most of the world (including the OR community?) will not know this, since OR is likely to remain hidden within a 'business analytics' agenda.
A new graduate who wants to practice OR should possess sound 'traditional' math and OR skills as well as the ability to work with large data sets locked in databases. You should be strong enough in your fundamentals to perform proof-of-concepts without asking your boss (typically one who cares not for OR or even knows what OR is) to shell out big $$ to buy you a new CPLEX or Gurobi license or a new SAS license to analyze patterns in data.
Familiarity with open-source tools such as COIN-OR and R will help since they are free for R&D. In such new industries, the ability to work with and analyze large volumes of messy data is perhaps more important, so being at ease there will give you an edge over non-OR types since you can 'take it all the way'. Remember, OR is an applied field that is tailor-made for analytics, and that is a powerful plus point.
A PhD would be preferable unless you are OK with being tagged as an OR-programmer/data analyst. Ability to communicate technical ideas with a non-OR audience in plain English is very, very important.
Business problems do not show up with "use OR" on it. The stalwarts of our field in the 1950s-1980s came up with original approaches that best suited the practical problem at hand, and using the best computing technology available, and these breakthroughs eventually became part of OR folklore and textbooks. OR best succeeds when it is explainable and insightful, and at times, a smart 10-line answer may just do the trick.
Finally, It's worth restating the obvious. The most important component of OR practice is that you build reliable solutions for real people who spend real $$ in a tough economy.