How should a Data Scientist's resume differ from an Academic CV?

How should a Data Scientist's resume differ from an Academic CV?

"My academic cv is very much focused on papers published and not so much on skills and experience. From what I understand a business resume should focus more on experience and skills. Any thoughts/tips on how I can rework my academic cv?"

Your academic cv is very coursework and research focused. You've heard business resumes need to be more action and results oriented, but you're not sure what that means for you. You're looking for advice on how to re-work your academic cv and not finding much advice out here. To help get you started, here are some thoughts on what you'll need to do...

  • Present your time in academia as job experience.. Academia cares about publications. Data Science Hiring Manager's care about relevant skillset and experience. As such, make sure any research positions you're listing include not just a description of what you were doing but how you were doing it (techniques, programming languages, software etc). Try at all times to explain the relevance of the research to the job. And, if its not relevant, don't include it (or devote very little space to it).

  • Highlight results. Hiring Manager's want to see that you are impact oriented - not just excited about academic research for intellectual satisfaction. This can sometimes be hard, so try to think of instances such as
    • process improvements
    • theorem proofs
    • real world applications
    • prototypes or innovations
    • IP
    • grants or funding secured

  • Highlight relevant business skills. Look for ways to call-out softer skills that you'll likely have developed and will be relevant for Data Science: teamwork, prioritization, communication etc. And do your best to use action words to describe these too (created, managed, coordinated, led etc.)

  • Include Teaching roles. Especially if they were in a quantitative subject as it is good proof of your ability to not only use relevant concepts, but also explain them (which is often harder!)

  • Talk about internships. Even if you do a good job of presenting your academic work as job experience, you still run the risk of being seen as non-business savvy. Any internship work you can point, especially where you can highlight businessimpact will help establish you as more than just an academic

  • Highlight your accomplishments. Whether its peer awards, funding wins, patents, winning tenure, etc. - any/all of these help you stand-out from the crowd as a high-achiever in your chosen discipline

There's also some things you definitely shouldn't do in your Data Science resume (in case you were tempted!)

  • Do not list out every paper you've written - just to know that you've written a paper in the field is good enough

  • Do not list out every conference you attended. In fact, its likely any will be relevant, unless you had a speaking or organizing role that demonstrates a clear strength the role may be looking for

  • Avoid technical jargon when possible - simplify statements so that your role is understandable

  • Don't make an overly big deal about tenure. Its great to have, but not so valued outside of academia

That should give you a good start, so time to get to it...

How to take action now!

Focus on presenting your time in academia as job experience. For a job you're considering applying to, figure out which of your research projects/papers showcase relevant skills, techniques etc. Try to come up with 3. For each, write an action and impact oriented bullet point that describes it: what you did, how you dit it, and with what impact. Your "academic cv" will soon be transformed :)

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