With the demand for Data Science professionals growing significantly since the past two years, we at Digital Source have seen an exponential spike in the number of applicants seeking to enter the field. Nevertheless, despite the varied levels of skills and expertise in the industry, the first step in discovering the perfect candidate always remains the same – stumbling across the right CV. With a majority of consultants in the field screening through CVs for an average of 7.4 seconds per CV, first impressions have never been more important.
We sat down with our Senior Data Science Consultant, Pascal Hennequin, who shared with us his experience, insights and top advice when it comes to building your Data Science CV. Read ahead for your ultimate guide!
Here’s where your CV stands out from the rest…
For recruiters scanning through hundreds of CVs, there are a couple of things that catch their attention. Some of the most common include the following; CV length (is the CV is too long, or too compact?), attention to detail (does the CV look messy or has the candidate paid attention to finer details?) Other elements include education, previous employers and skills (for example coding languages).
Highlighting those technical skills
Display your skills through a metric. For example; by rating each skill on a scale from 1 to 5. A metric helps recruiters give context to the size and scope of the work that you did. It’s important to think about the key performance indicators, or KPIs, for your role. Think about which ones represent your contribution to the company and the ways in which you helped it grow. Depending on your role, this might include: new technologies, tools, (coding) languages and / or processes adopted.
How to successfully lay out job experience
While there isn’t a specific formula to successfully laying out your job experience, there are a few crucial points to take into consideration here. Make sure you list your previous experience to appear organized and neat. Ideally, you would also like to see an explanation of the position, responsibilities, a timeframe from when you worked there and maybe a reference. Nice add-ons would be a quote from a colleague or manager, or a link / presentation to a certain project that has been put into production.
Customizing your CV to opportunities in data science
To a certain degree, it’s always good to adapt your CV to a job opportunity. However, it’s equally important to stay true to who you are – so make sure not to exaggerate. For data scientists, a candidate could highlight their CV more towards data analytics or machine learning, depending on the opportunity of course.
Common pit-falls and how to avoid them
Too often, recruiters come across many CVs without a professional picture. Many clients appreciate a strong profile picture from a candidate, so make sure to include one in yours. Another key tip! Pay close attention to aspects like formatting and structure – a CV should be easy to read and understand. Recruiters will determine whether your profile is useful or not, in a mere few seconds. If a CV has no proper structure or format, it’ll probably not be reviewed at all!
Including side projects and other accomplishments
It always helps to add details like side projects and accomplishments. However, make sure to keep it line with the role that you’re applying for. Again, don’t exaggerate. Your CV shouldn’t be too long either, ideally two pages.
Our quick-fire CV advice for candidates looking to break into the field
- Use an online tool / template to make a comprehensive and structured CV. Websites like Novoresume.com or Upwork.com can be very helpful.
- Use visuals to highlight your skills and attract attention. Tools like Photoshop or Illustrator can be very useful! And maybe consider a profile picture?
- Don’t make your CV too long, two to three pages ideally. Showcase your CV with short sentences and appropriate achievements!
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