So, you want to be a data scientist? Great choice! On average, data scientists earn over USD 110,000 per year in the US. Next to that, you have the chance to work with some of today’s most exciting innovators and tech pioneers. However, data scientists have to go through some of the most rigorous and difficult hiring and testing processes. Nevertheless, once you’re in, you will be enjoying one of the best employee experience fields with a bunch of potential for progress and an intellectually-stimulating career!
The first thing to do so, of course, is to craft an awesome CV. With data science CVs, it works a bit differently than the templates or generic advice you might find online. Recruiters are looking for specific skills and traits. There are some specific sections to include and mention, but there are also some general tips to stick to. Let’s have a look!
Keep it Brief
Submitting an extensive resume that’s several pages long will do more harm than good. If you ask any recruiter or HR manager about this, each and every one will say that short and sweet CVs are preferred. That’s even more apparent in companies that get hundreds of applications each day.
The ability to keep it short shows your capacity to set apart the important from the irrelevant, which is a must-have for a data scientist. It also shows that you don’t have to boast and embellish your achievements with too many words. Instead, your work speaks for you. Refer to the section below on how you should objectively and clearly present your prior work experience.
Create Custom CVs for Each Company
This is an absolute hit among recruiters and HR team members. If you show that you took some time to actually tailor your CV to the specific company you are applying to, it will definitely not go unnoticed. Submitting a custom-made CV that specifically describes your potential contribution to that company is a sure-fire way of showing how serious you are about getting a position there.
However, it’s obvious that this can be very time-consuming. This gets even more challenging if you are planning to play the field and apply to dozens of different companies. In this case, what we would recommend is to make a list of your top choices. Then, set aside some time to create a custom CV for the, for example, the first three companies on the list. For all others, you can send your generic data scientist CV.
Work with Experts
If you dread the process of crafting a CV, there are a lot of services nowadays that can do it for you. Not only will you get rid of a dreadful task, but you will also get an optimized CV with the best chances of success. Use some of these services for help: order dissertation online (when you contact essay writers for work, you can also ask for a professional resume), Elite CV (a service for creating CVs for experts to land top jobs), getgoodgrade.com (get expert help from pros for your data science resume).
When you craft a creative CV full of different styles and font, the recruiter can receive something entirely different. That’s why you can use Markdown, a text editor that will preserve all the styles in your CV document.
When you send your CV to the HR department, they can open it on God-knows-which computer, OS or word processing program. With Markdown, it’s guaranteed that your recruiter will receive exactly what you send out in your final file.
Add Some Pet Projects You Are Proud Of
Finally, employers want to know about your education, skills and work experience, but it’s always great to get to know a person on a deeper level. One of the best ways to provide this information in your CV is to mention and describe pet projects that you have worked on. They do not necessarily have to be super-successful or well-paid projects. The point of this section is just to show what you are personally interested in – what you do in your free time.
This can even go beyond data science and the industry in general, but don’t stray too far away. If you get into a 4-paragraph rant about how you’re politically active and a passionate protester, you will break the first rule of keeping it short (refer to section 1). Instead, you should aim to find a way to add some of this interesting information in an appropriate and relevant context within the CV, without going too far and wide.
Describe Specific Tasks and Results
When you’re describing your work experience, make sure you’re clear on what your tasks and role was in those projects. Also, accentuate the positive sides of each project by boasting some of its results.
A great formula you can use in these sections is “Position + used ‘which tools and strategies’ + in what way + to achieve ‘what’”. This is a CV work experience formula that was made popular by Google, which requested that every CV sent over to the company be formulated in this way.
Make it Effective
In other words, make it “wow”! You don’t want to leave your recruiter or whoever will be reading your CV in a mood like “meh, we might as well call this person over for an interview”. Rather, they should be thrilled to do so and can’t wait to meet you in person. An effective CV can open many doors.
To see whether your data scientist CV is effective and powerful, put yourself in the employer’s shoes. Would you be impressed by your CV if you were in their place? What would you have to change or add to make it really pop?
The first step towards landing a perfect data scientist job is sending out a jaw-dropping CV. This relates to everything from design, writing, to the way you present your experience. Remember, recruiters receive dozens of applications on a daily basis (even much more in popular companies) and you have to find a way to stand out.
It may be tempting to write a detailed, extensive CV of 5+ pages, but that will not have the positive effect you imagine it will have. Rather, before you send over your final resume, make sure you eliminated everything that does not have to be there. Then, narrow down your expertise and skills by describing them in an objective and precise way. Finally, try to form a tailor-made CV for each of the top companies you are applying to.