Optimising a Data Science CV

The job of a data scientist / analyst is to present data in a way that is as a minimum both useful and accessible. Probably engaging too, though some might see that as a slightly lower priority.

Now, a CV presents data 📊 – so as someone in data science your CV had better meet those requirements. Does that mean it’s different from the A4 1-pagers I usually see? I think so, in small but important ways.

My Big Data and Artificial Intelligence training at M2i requires me to find an internship, and I’ve updated my CV accordingly with support and guidance from our coach Florian Masoni. The end result is at cv.sneezingtrees.com, and I’m going to share my thinking behind a couple of things I did with it. I’ll be the first to acknowledge that it’s not perfect, and you’re more than welcome to be the second, third, fourth, etc. – indeed all feedback is appreciated – but at least I feel like I’ve followed a thought and design process in line with my chosen profession.

Here’s the summary of what I did and why:

  • Format. These days, the CV has to work on 💻 desktop, tablet, mobile 📱 and in print (A4) 📄. That means two versions as a minimum – one conventional A4 pdf that works (just about) on desktop and tablet, and a second “digital” version optimised for mobile. Both can then be posted as featured items in your linkedIn profile. For me, that “digital” version should be a cut-down large-font image-rich version in portrait layout. That’s what I would have done, were I not a former web developer. Instead, I decided to create an online responsive version that adapts to screen size and also offers a downloadable A4 pdf for printing. (Obviously a web version is not for everyone, and perhaps not even for web developers as it’s a lot more work than a “digital” version.)
  • Multiple levels of information. It’s clear that recruiters want no more than one A4 page, so that limits how much information you can convey. Or does it? My thinking is that a single A4 page is the rule because that gives recruiters enough to do an efficient first pass. Any more and you’re wasting their time. However, if that review of first level information is positive, then perhaps they’d like more on an opt-in basis – second level info. Concretely, this means links within your CV – links to blog posts 📝 you’ve written, examples of projects on Github, companies you’ve worked at 🏢, whatever. That’s also why I’d choose pdf over jpg / png for the “digital” version – to allow the inclusion of links. (And yes, of course links won’t work if someone actually prints your CV, but I’m betting that’s rare nowadays.) For a web version like mine, I can use links that open popups – second level info – and then links in the popups to content elsewhere – third level.
  • Graphics. To communicate certain concepts or messages, text just doesn’t do it. In such cases a data analyst / scientist should be able to come up with a graphic. Only where appropriate and useful though; images that communicate nothing are a distraction, a waste of space, and worse, they imply you lack the laser focus a data wrangler should have. I created a graphic that maps what I call general competencies (focusing mostly on soft skills) against certain elements of my experience. I think I could do better, but I do at least like the visual effect of showing breadth and depth – which was the message I wanted to get across.
  • Meaningful proficiency evaluation. The advice is often to give a level of knowledge against technical skills, indicating (for example) beginner, competent or advanced for each. But how to know what level you are, particularly when you’re relatively inexperienced 🤔? And what’s more, does a recruiter have the same definition of “advanced” as you do 😬? As data analysts, we should know better than to use a scale that’s open to interpretation. So here’s what I came up with – a proficiency scale that’s generic to anything technical. Of course it won’t fit on the conventional 1-pager, but you could always include a link to it elsewhere (on Google Drive for example). And for online CVs like mine it’s easy to integrate it dynamically 🚀.

To finish…

The end result is not drastically different from a normal CV, apart from the dashboard-style layout for my online CV. It’s just a few tweaks that make for more efficient and effective communication 🎯.

And in fact I now think my dashboard layout was perhaps not the best design choice 🤨. Were I to do it again, for large desktop screens I’d try to get all elements at a legible size without any scrolling needed, arranged to suit the landscape format. That way, a recruiter can see everything all at once, just like they can on a printed A4 sheet, without having to click in and out of sections. For smaller screens, maybe the dashboard model is needed. As always with CVs though, there’s a limit to how much time we can sensibly invest, and I hit mine a while back 🙄. So now it’s your turn – how will you tweak your CV to reflect your data science aptitude?

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