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How to Transition from Academia to Corporate Data Science

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A 2015 Moody’s study predicted that the closure rates of small colleges and universities will triple in the coming years and mergers will double. This rate could lead to 15 institutions a year shutting their doors for good.  The pursuit of tenure track academic positions becomes more difficult and more risky as a result.  Opening your job search from just academia to include business positions will increase your options dramatically.  What can you do to prepare for this very different job search process?

 

While in Academia…

  1. Get real world experience. Have a local company provide data and a project for one of your classes to solve. Get a consulting assignment as part of your research. Answer real marketing and business questions.
  2. One of the biggest complaints about academicians transitioning to corporate America is their lack of experience with large datasets. If you have to, enter a Kaggle or KDNugget competition to get data experience. Use SAS, R, SQL and Python. Get good at one or all four.
  3. Not required but definite pluses on corporate resumes is experience with MapReduce and Spark, Hive and Pig. Machine learning and AI are very hot, too.
  4. Hiring managers know you’ve done well in your educational training, you have to convince them of your communication skills. Practice by presenting your research at conferences and to your associates. Have them ask you hard questions and prepare answers to those questions. Record yourself when you are teaching and assess how clear and articulate your presentation is. If you find yourself lacking, work on it!

 

Before Interviewing…

  1. Convert your academic CV to a professional resume. There is no bigger tip off to corporate America that you don’t understand the difference between academia and business than submitting a ten page CV. A two page resume is more than adequate if you understand what is expected of you.
  2. Research the company, the job and the people you will be speaking to. The internet is a wonderful thing for the job seeker, but it raises the bar on the level of knowledge you should have before actually interviewing. Research is your strength or you wouldn’t even be in academia. Make it work for you in a job search.
  3. Practice the top 25 interview questions. Yes, you may not get asked these exact questions but by preparing answers, that you can articulate in complete sentences, you will be very well prepared for variations on these 25 questions. For every computer and analytical skill you have be prepared to discuss your level of skill and one or two projects where you applied it. Remember experts almost always say they aren’t experts as they are so good they know what they don’t know. Be honest about your strengths and weaknesses.
  4. Find a recruiter in your specialty. Yes, you can apply to many jobs online through the company websites, LinkedIn and Indeed, but the right recruiter can get your resume out of the black hole it may go into from an online application. The right recruiter can also advise you on your resume and what skills to emphasize.

Prepare Your Mindset…

If leaving academia is a disappointment, get over it! Applying for a corporate position, while still pining away for an academic job means you won’t get the job offer. Whether your institution is going through some belt-tightening or closing, or your research floundered and no tenure track was going to be forthcoming, you have to approach the corporate job search with full enthusiasm. Candidates who talk about their preference for research or teaching are not recognizing and not selling the value the have to corporate America. Chances are your education, your intelligence, the skills you’ve gotten while pursuing your advanced degree will be very valuable to companies, but you have to be convinced of that before you can convince them.

Interested in making the transition from academia to corporate America? Contact Smith Hanley Associates Data Science and Analytics Recruiter, Paul Chatlos, at pchatlos@smithhanley.com.

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