If you have an analytical background, you probably haven’t had many difficulties attracting a recruiter’s interests.
The demand for Data Scientists has soared, but the number of Data Scientists out there is nowhere near enough to meet it: annual AI-related hiring has grown 74% since 2015, but global tech talent shortage is projected to reach 85M by 2030 (Source: https://quanthub.com/data-scientist-shortage-2020/).
Given the blooming job market, you might think you don’t need to put much effort into your CV. As long as the relevant stuff is in there, people won’t care, am I right? Wrong.
While it might not be too hard getting your CV noticed and get through the first screening phase, from the moment it lands on the hiring manager’s desk, you only have a short amount of time to impress (You probably have more than 6 seconds but not much more). Chances are they receive tens of CVs to evaluate every day, and are going to skim through them very quickly.
For a long time, I have been in the candidate seat and blissfully ignorant of what would or wouldn’t impress people in my CV. Over time, I have been given excellent advice by mentors and learned what to look for when you read a CV from them. And these things can make a lot of difference.
Here are seven tips that will make your CV stand out.
1. Put your relevant work experience at the top
When someone reads your CV, they obviously start from the top. That’s where you want the most relevant information to be. Those few lines will make someone decide whether it’s worth keep reading your CV or skip to the next. So, if you have relevant work experience as a Data Scientist, put it at the top.
2. List your most recent role first
It is a good idea to list your work experience starting from the current or most recent one. Again, people will only spend a few seconds on this section, and the takeaway needs to be that you are a Data scientist currently or have been until not long ago. The reviewer does not care about what you were doing five years ago. People change careers and five years in Data Science is an eternity.
3. Be succinct
Time is critical. A reviewer will not read your current role description if it is a densely written 10-line paragraph. Write max two lines to give a general description of your role’s responsibilities and use bullet points to refer to specific projects or achievements.
4. Bullet points are your friends
Bullet points, when used properly, can be powerful tools. They break up complicated information and make it easier to read. But, don’t go crazy with them; aim for three to six bullet points per section. If you have more items, try grouping them into single projects that encompassed them or select the ones you think showcase your talent the most.
5. Quantify your achievements
Present your achievements in a business-oriented way by quantifying the value they delivered. Even better if it is a tangible result. For example, an increase in revenue, a saving in expenditure, or improvement of existing processes. Be detailed. It will showcase your ability to understand business needs and deliver on them.
6. Pick your skills
You might want to trim the list of skills in your CV. Listing 20+ skills forces the reader to filter for the ones that matter to them. A better idea would be to make the skills explicitly listed in the job description more prominent — if you have them — or only select the ones in which you are most proficient. Focus on the keywords whoever reads your CV will be looking for. If they are looking for NLP experts and you are one, then make it easy to find and state it clearly.
You can put the rest under Other skills. But anyway, do you absolutely need to list X as a skill? For example, how useful is it including C++ as a skill if the last time you coded with it was ten years ago during grad school? Unless C++ is a required skill, not much. Also, some skills are a given, and you don’t need to call them out. Who doesn’t know how to use Office in 2021?
7. Limit the personal info
The truth is the reviewer does not care that you like traveling or playing futsal in your free time unless your hobby has something to do with the job to which you are applying. For example, if the role is for a betting company, then familiarity with sports might give you the edge over another candidate. It’s going to be a small edge, though, as, in data science, skills are transferable, and domain knowledge is not necessary.
Personal achievements, however, can be different as they might tell something about you. If, for example, you trained for a marathon, it might say something about your commitment or long term planning ability.
In general, do not underestimate the importance of a good CV. It’s the first step to your Data Science interview. Keep it up to date and no more than two pages long — or three, depending on experience. The most important information should be on the first page, though. The second page of your CV is like page 2 in Google Search Results. Almost no one ever goes there.
The list above comes from my own experience and is by no means exhaustive. **** If you have tips that you’d like to share, please get in touch or leave a comment below!