
In this article, I will walk you through the exact resume that got me multiple data science interviews and job offers and give you my most important tips along the way!
The Resume (at a glance)
If you haven’t got time to read through the whole article, this is the resume/CV in its entirety.

I have anonymised certain parts for privacy reasons, but this is literally the resume structure and language I used when applying for data science roles.
If you want to get this PDF template, then check out the link below!
Let’s now break down each section along with top tips!
If you want this template, this resume is built from Overleaf using a template created by Timmy Chan. Chan is credited with the template; you can check the source code here on his GitHub. The template and code are under a Creative Commons CC BY 4.0 licence.
Design & Layout
First, your Resume/CV should be one page long. On average, a recruiter will look at it for 6–8 seconds, so make their lives easier by making it one page to avoid unnecessary scrolling.
Secondly, I recommend you write your resume using LaTeX, a software for typesetting documents. I think LaTeX looks much nicer than Word or Google Docs; it is way more flexible and customisable, and as data science is an industry with many academics, they will appreciate the effort in writing your Resume in LaTeX.
LaTeX is also a great skill to have, regardless of whether you are creating a resume. I use it every time I write a blog post to produce mathematical equations, when creating invoices for side projects, or when writing one pagers in my job.
I recommend getting started using Overleaf, which is completely free and contains many resume templates and much more. That’s where I found Timmy Chan‘s template for my resume!
There is a slight learning curve, but it will only take you a couple of days to become proficient, it’s pretty intuitive, especially for some small projects like a resume. I recommend starting with the Overleaf tutorials and working your way through those.
Of course, if you ever get stuck, ChatGPT can quickly help you overcome any problems you may have.
Finally, keep it clean and simple. Avoid using lots of small text; make it as easy to read as possible with consistent formatting throughout. Simple and elegant is always the best approach for a resume.
Header

This part is straightforward; the header should include your contact details and any useful links to demonstrate your work, experience and external projects.
I recommend adding links to your website or projects to showcase your interest in the field right from the start. These links likely contain more information about you than the resume itself, so you ideally want recruiters and interviewers to look at them to learn more about you.
These external links will also give you something interesting to discuss with the interviewer later if they find something interesting in your past work.
Some people often add an "About" or "Summary" section here. These are a bit redundant and just waste space in my opinion. If I am applying for a data science role, it’s obvious, given my background and experience, that I am a data scientist with specific skills. I don’t need to write it out explicitly.
Unless you are changing Careers, maybe that’s when an "About" or "Summary" section could be helpful, but otherwise, it’s probably unneeded. However, I am sure some people will disagree with me on this!
Technical Skills

The next important thing is listing your technical skills. This is like a very, very short version of all your abilities.
This is high on the resume because it will immediately help the recruiter know if you meet the job’s technical requirements. It is almost like a checklist that is over and done with early on and should increase your chances of getting through to an interview.
A few things I recommend you do in this section are:
- Don’t list too many things; this looks suspicious. I would be sceptical if you listed something like "Python, SQL, C++, Rust, R, Assembly." It seems like a bunch of buzzwords, and I would find it highly unlikely you knew them all to a reasonable level.
- When it comes to coding abilities, it’s best to use language like "proficient" or "familiar with." Avoid using arbitrary star ratings like "Python 4/5" or claiming to be "advanced." This way, you’re setting realistic expectations and ensuring that your skills are accurately represented.
- Don’t write out every Python package you know. I see this a lot, but it is really unneeded. If you are applying for a Data Science position, I already presume you know numpy, pandas, and matplotlib; there is no need to explicitly state it!
- Most importantly, don’t lie. It’s a known fact that people exaggerate in their resumes, but you don’t want to list any skills that you can’t back up without evidence.
Experience/Projects

Next up is experience or projects if you haven’t worked any relevant jobs for being a data scientist yet.
The most important part here is to demonstrate exactly what you have done at each company and what the outcome was, ideally using numbers and figures where possible.
Don’t try to be too humble; really "flex" the work you have done and the impact you have produced. Again, obviously, don’t outright lie, but make sure to showcase how great you are.
For example, notice here how I discuss technical steps like "ARIMAX" or "XGBoost" with the goal of better forecasting or predicting something and finally state the financial value or metric improvement.
This shows my technical abilities and that I think of business impact with my projects, which are crucial skills for any data scientist.
Feel free to mention other softer skills as well. I discuss working in a cross-functional team and leading sessions like a data science journal club. However, the technical deliverables should take precedence, particularly for junior roles.
If you haven’t got any experience, replace this with a projects section and carry out similar wording regarding the technical and business parts. Try to list projects most relevant to the role you are applying for to demonstrate an interest in that particular area.
Education

Now we are onto education. If you don’t have any relevant work experience, I recommend putting the education section before work experience and then a projects section after.
As I have a few years of experience as a data scientist, my education section is pretty simple. I keep it in as many data science job adverts explicitly state a need for a master’s degree in a STEM subject. So, I have mine to fill that check box right off the bat.
However, if you don’t have experience, you should flesh this education section out and discuss any relevant work you have done in your degree for a data science job.
This is especially true if you have done a data science or Machine Learning master’s. You probably have loads of relevant coursework that you can mention here to showcase your knowledge. Try to make the wording of these projects similar to that of the experience section above.
If you have a science or maths degree, you might have done a coding or statistical analysis project. Again, this is very relevant to a data science position, and you should definitely include this.
Personal Interests / Activities

To ensure you are a normal human outside of work, I’d always add some personal interests at the bottom. It may even lead to an exciting talking point with an interviewer, which is always a good thing!
I say this, but two of my activities mentioned are my blog here on Medium and my YouTube channel! I include these as they are unique and would make me stand out and demonstrate my interest in the field.
I do mention that I play hockey, so I am not purely some data science robot! And, you never know, maybe my interviewer or recruiter also plays hockey!
Two or three bullet points are enough in this part and try to add things that you feel make you unique and stand out compared to other candidates.
Summary
To summarise the key points:
- Make it one page; recruiters don’t have time to read multiple pages.
- Make it in LaTeX using one of the templates from Overleaf; I prefer the look of LaTeX to Word or Google Docs.
- Add a skills section at the top listing all your relevant technical abilities.
- Use numbers and figures in the experience section to concretely demonstrate your impact.
- Add any relevant projects if you lack relevant experience, and explain them.
- Add in your degree, particularly if it is a STEM subject, as many data science jobs list this as a requirement.
- Hobbies and activities are helpful to show you are not a robot and demonstrate your uniqueness.
Another Thing!
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