It's October, so I'm back on the college recruiting trail. This year, OpenBI's interviewing at two top Midwestern universities. I really love this part of my job, though it's pretty challenging, especially now with the down economy. Even in a recession, it's still important for services firms like OpenBI to anticipate demand 6 to 12 months out and make hiring plans accordingly. It takes at least that long to ramp up even a top college grad to the demands of technology consulting.
So far, sign-up for interviews with OpenBI by qualified quantitative/technical/business types has been gratifyingly through the roof, causing me no shortage of angst as I make decisions on whom to interview among equally qualified candidates. While I'd like to think this has all to to do with the cachet of OpenBI, in reality, the demand for new college grads is still soft. And the luster of financial engineering, over the last few years the field of choice for many top quants students, is now ebbing, a not surprising victim of the financial crisis. Indeed, this cycle reminds me a lot of 2000, when the high-flying technology consulting career choice crashed and burned, no doubt mirroring the same fate of the Internet boom. What's the next hot field, Health Care? Green? I'm not sure, but OpenBI's happy to be the beneficiary of this (hopefully) short term anomaly.
There've been many changes in the twenty years I've been doing college recruiting. Back then, we'd look for computer science, engineering, or information systems majors, preferring a known technology focus, even for BI candidates. As students became more computer literate outside formal college courses, however, we started finding qualified BI candidates everywhere. One of the best I ever interviewed was a political science major with no university computer-related training, who self-taught Perl, relational databases and HTML, earning enough money managing data-driven websites to put himself through college, even as he researched his honors thesis. This candidate was certainly BI-ready at graduation. Other students studied mathematical esoterica in college, having honed their programming craft in high school. We've often found students who aced the high school Advanced Placement test in C or Java, then went on to learn web programming on their own and make a ton of money in college. Works for me.
I guess I should have known something was a bit amiss this year when my resume search for Information Systems at State U yielded no hits. Not one. I found two computer science majors, one of whom had already graduated. Granted, not all resumes were in when I searched, and my source of resumes was but one of several, but still....Fortunately, I was able to find many capable candidates after getting creative with my searches.
Private U seemed to have an almost limitless supply of mathematical economics/social scientists with with top-tier resumes except, for half or more, an indication of programming skills. With all the mathematical sophistication they presented, I was flabbergasted that so many had little computer skill to highlight outside Microsoft Office.
Maybe I'm just an applied bigot, but I need to at least understand at a high level the programming algorithms behind the quantitative theory I use. I also like to assemble toy examples of such methods in languages like R or Ruby to help my learning. That's why I was ecstatic when I came across the splendid OpenCourseWare programming course by two eminent MIT professors. If a programming curricula's good enough for MIT, it's good enough for anyone. I now recommend this course to students who wish to learn programming outside class with the agile, easy-to-learn open source Python. I sure hope the needy with otherwise exemplary resumes can find the time to invest in it.
Note to third year students: We love what we're seeing with the quantitative emphasis on business and social science at top schools today. Your mathematical and economic sophistication is first rate -- an ideal background for BI. By all means, take differential equations, machine learning, financial economics and econometrics. Learn decision theory, quadratic programming, mathematical statistics and advanced regression. Pass actuarial and CFA exams. More and more, we're using this stuff in BI, much to the benefit of our customers.
But mathematically sophisticated students, do yourselves and your future employers a big favor: make sure you know how to program when you leave college algorithms and data, loops and lists, conditionals and hashes, modules and packages, objects and methods -- you choose the language. Programming's an absolutely essential foundation for BI. I'll trade a solid Java (or Python or Ruby) background for an advanced math course any day. And while you're at it, could you become familiar with the basics of relational databases and SQL? That's how we communicate with data in BI. It doesn't matter how you learn. Formal coursework is fine, but so is self-learning. Indeed, so much the better if you can pick up skills on your own outside class. OpenCourseWare presents nothing short of phenomenal learning opportunities. Programming skills will make your quants coursework so much more pertinent; you'll be able to put it in action immediately. You'll also be ready to turbo-charge your ramp up in BI consulting. See you next year.
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