What does this even mean and why are people putting it on their CVs? 🙂
Towards the end of 2020 I was lucky enough to be hiring for several new positions in my team1. Given the times that we are in, there are many more applicants for roles than there was even a year ago. I’ve spoken before about the skills that you need to get a role as a data scientist and there are specific things I expect to see so I can judge experience and competency when I’m looking at these pieces of paper so I can decide who I want to interview.
Sadly I’m seeing a lot of cringeworthy things on CVs that are the fastest way to put a candidate on the no pile when they reach me. These things might get you past HR and also past some recruitment agents, and I wonder if this is why candidates do them. I try and give as much feedback as I can, although sometimes the sheer volume of CVs and the time taken for constructive feedback would be more than a full time job. By sharing some of these things more publicly I hope to pass this advice on to as many as possible.
Overstating your accomplishments
It’s important to highlight things you’ve achieved at all levels, but showcasing someone else’s work as your own is a huge red flag. This might get you onto the maybe pile when you reach a company, but be prepared for someone to check before you get even a telephone interview, Here are some of the bad things I’ve seen in the past few months:
- Stating you have created models when all you have done is get the code from github and run it locally: I’ve seen this with the paper references in the form “Created a model to do something cutting edge, published in journal, title“. At first glance it looks like a list of papers written by the candidate themselves and is very impressive. Then you find the papers based on their title and see that the candidate was not one of the authors of the paper. Every time I have seen this all they have done is get the code. At it’s absolute best, this is a naïve attempt to mislead, at worst it’s deliberate fraud. If you have actually created a model based on the maths in a paper rather than just downloading code then be clear on what you have done and never try to suggest you have done more through lack of detail.
- Claiming you’ve written papers that you haven’t: Yes, I’ve seen this. Someone with a similar name and the CV has been padded with the accomplishments. We check. It’s not difficult to find the actual authors of papers and validate whether this matches the CV or not.
- Overstating what you’ve done in a role: This is very frustrating as it’s often until we’ve made the time to do the interview that we find this out. If something is on your CV, particularly if you say you were the key person, expect to be asked in detail about it. It’s a waste of everyone’s time if during an interview every question we ask results in an admission that you didn’t do as much as your CV suggested.
- Claiming qualifications you don’t have: I’ve seen CVs where people claim MScs or PhDs from universities that they’ve never attended. We check.
One of the key skills I look for in a Data Scientist is integrity. If you deliberately mislead on your CV then I cannot trust you and there is no way that I will let you get anywhere near my company’s data.
Showcasing your skills poorly
Showcasing your skills is really the point of your CV 😉 – show me what you know and what you can do. It’s really useful to have this up front – a summary of what you know well and where you might need training. However, what I’m seeing a lot of is candidates rating themselves on an arbitrary scale, usually 1-5 (as numbers, coloured dots or boxes). I get why people might think this is a good idea. When I started out in tech, it was customary to put number of years experience in a language. This makes no sense for new technologies – you could be the world’s leading expert on something but only have a year’s experience! It also had the problem that you may have used something many years ago and through lack of practise not be as fluent in coding as you once were.
However, without any sort of scale to tell me otherwise, I have to convert your coloured blobs into something. Do I do this linearly? Exponentially? Sigmoidally? What does 5/5 mean compared to 3/5? How will I know what you know or can do?
Well, as my primary school daughter would tell you, 5/5 = 100% . So if you say you are 5/5 then you are claiming that you know everything about that topic. Except for a very few individuals, this is plainly rubbish. I’ve seen several people with less than a year out of university state they are 5/5 on “data science, machine learning, deep learning, python, and SQL”. Every time I skim the rest of the CV in case there is evidence that they have done large amounts of personal projects to justify these claims, but it’s just not there. Getting a degree in computer science (or doing an MSc with these modules) and doing a single project does not make you an expert in everything in that topic.
I’m sure most of the people who put 5/5 on their CVs are actually 2 or 3 out of 5 in terms of technical knowledge: “comfortable with the topic and able to find the information they don’t know within limits of their experience”, which is not a bad thing2. So is their score a measure of confidence rather than anything else? Or is it a fear that nobody would take a look at them unless they are at least 4 out of 5? The problem is context. If I’m making the above assumption to your 5/5 for python and I start asking you about some of the issues with library compatibility on very old installations, you might be surprised. Either add a definition of what you mean or choose a different way to describe your skills. More often that not if I have a CV where someone is stating 5/5 across their skills but has no other text to explain their experience then they’ll go straight to the no pile in favour of someone who has taken the time to showcase their experience.
I know the job market is fierce at the moment and everyone is doing everything they can to standout, take the time to show what you have done and how this compares to both the job spec and what the company does. Finally, please be honest with your CV/Resume.