A great CV might get you an interview. A bad CV will be thrown away!
That's the importance of a CV. Often it's the first impression that an employer has of you, so forget about making it good, your goal is to make it outstanding.
Now, I'm far from an expert on writing CVs. I'd be lying if I said that I had an above average success rate when it came to job applications. In addition, there are already several articles out there on the web that will give great guidance and tips on how to write a CV. In fact, one of the best articles I've read giving advice on a data science CV was written by the head of advanced analytics and data science at royal mail, Ben Dias, titled "What do hiring managers looks for in a data scientist's CV
So why am I bothering to write a post on CV advice?
A few months ago I was asked to go through CVs and help shortlist candidates for interview for a data science role at my company. This was incredibly insightful for me. I saw certain things that really stood out, both positive and negative. So I want to give you some tips based on what I learned from that experience.
1. Don't undersell yourself
Remember, you're trying to stand out for the right reasons. When an employer looks at your CV it's usually not in isolation. They have looked at many CVs, so you're being compared to others, some of whom won't be afraid to exaggerate a little to make themselves stand out. It's an employer's task to filter out those that are exaggerating the truth. However, given the choice between a candidate with an exaggerated CV and a candidate with a CV that undersells themselves, I'd probably hedge my bets on the candidate with the exaggerated CV.
Let's be clear, I'm not saying that you should lie on your CV, but don't use language that could give an employer reason to think that you're not as good as you actually are.
For example, let's say you've done a few online data science courses and you've been programming in Python for about 6 months. You're unlikely to be a world expert Python programmer. But that doesn't mean that you should state that you're a ‚??Python beginner' in your CV.
What level is a Python beginner? Is it someone that doesn't know how to write a for-loop or someone that's had minimal exposure to machine learning in Scikit-learn? I think they're very different levels of exposure. The person using Scikit-learn can probably navigate the machine learning package ecosystem but the for-loop beginner may not even have seen a terminal/command window. You never know how an employer will interpret "beginner".
So remember, you don't have to exaggerate your skills, but don't use language that could be interpreted below your skill-set. Don't undersell yourself.
2. Focus on value
As a data scientist it's easy to overestimate the importance of algorithms. There are countless tutorials on how to implement various ML models and new neural network architectures solving different problems seem to appear every week. But the reality is that the models are only a fraction of what makes a good data scientist. Ultimately you have to show that you understand and care about business value.
I remember coming across a CV of a candidate who supposedly had 10+ years of data science experience. The CV was littered with data science buzzwords. This candidate had used a variety of algorithms to complete work projects and had very good evaluation metric scores. Great. But there was no mention of business value added anywhere. How much additional revenue did this project generate? What was the efficiency gain for the 90% classification accuracy? I could go on.
The points on your CV for a particular project should read something like: "I used technique X which achieved Y score which translated to Z value".
If you can't connect the outputs of your model performance with added business value then you'll lose huge brownie points.
Note: If you're applying for your first role then this isn't so much of an issue, but if you are already working in industry at the moment then you need to be thinking about how your current projects are providing business value and increasing the attractiveness of your CV for future employers.
Remember focus on value.
3. Showcase yourself
Obviously the CV should be a glowing story of you and your skills, but it's 2018 people! You can go beyond your CV. You might have a blog that showcases your data science skills or you might have videos that show you giving fantastic presentations. At the very least you should have a couple of projects on GitHub to show that you have a decent base level of technical competency. There should be links to all of these things in your CV. There's no guarantee that an employer will look at it but you've given them the option. Who knows, if they do look at it you might just impress them. This is what happened with one of the CVs I looked at.
One of the candidates had linked to their blog so I decided to check it out. I saw some great articles in there, one of them was on a topic that I was unfamiliar with. I could immediately see that this candidate had good technical knowledge and good written communication skills and so I advised that this candidate should be selected for interview (the other parts of the CV had to be good of course).
So don't be afraid, showcase yourself.
4. State your relevant skills...early
When you look at a job specification they'll almost always have a section on 'desired skills' or some variation. It may sound obvious but these skills should match the skills that you have written in your CV (you don't have to match all of the skills, even 50% will be fine in many cases). So make sure that you state these skills in your CV.
I personally like a "technical skills" section in a CV because it's easy to look at and scan through the list to see what skills a candidate has without trying to dig through lots of project work. Although some people disagree on this. It's quite subjective.
But whatever you decide to do, make sure a good chunk of these skills can be found in the first quarter of your CV.
About 6 months ago I looked at a CV of a friend of mine who was a post doctoral researcher. I knew already that they possessed the base skill-set required for a junior data scientist, however, their CV didn't tell that story. I had to get to the second page of the CV before I found any relevant technical skills. An employer is unlikely to have that amount of patience. That CV would've likely been thrown away at many companies before anyone got to the relevant parts.
So remember to state your relevant skills... early.
5. Get multiple people to read your CV
Let me put it like this:
If you're the only person that has read your CV then it's a bad CV!
Perhaps that statement is a little harsh but I say it for good reason. A CV is not designed for you, it's for someone else‚??-‚??the employer. So even if you love your CV, the person you need to like it may not.
Get feedback from a few trusted people. Don't be afraid to hear criticism. It happens. Your CV is unlikely to be perfect on the first draft. Showing it to people before sending it off will improve it and most importantly, improve your chances of getting to interview.
My own CV has been critiqued by 3 friends of mine and that's since I landed my current job. Some have very differing opinions about how it should be written (one thinks it should be 1 page and another says it doesn't matter if I spill onto 3 pages), so I've had to manage what I pick and choose, but on the whole, the feedback has massively improved my CV.
So don't send out your CV until you get multiple people to read your CV.
As always, this is not an exhaustive list of the tips for creating a CV but these are things that I noticed when looking through candidate's CVs myself. I hope this has been helpful for you. If you have any other tips, comments or questions, please feel free to leave a note below.
Thanks for reading.
The article was originally published here