Data science is all the rage.
It’s hot. It’s trendy.
And it also pays well.
According to Glassdoor, “Data Scientist” tops the list of the best jobs in 2019, with a median base salary of $110,000.
It’s not just that they pay well, data scientist positions are in high demand too - 6.5 times as many data scientist positions were posted on LinkedIn in 2018 than in 2012. Talk about rapid growth!
Of course, when there’s a great offer, there are also a lot of takers - specifically, good professionals with strong qualifications and rich experience.
Even to get to an interview, you’ll need a great resume - and we’re here to show you the best data scientist resume examples.
Here’s a data scientist resume example:
To start things off, here’s a data scientist resume sample built with our platform - you’ll soon understand what makes it effective.
This guide will teach you:
- How to create a data science resume that attracts recruiters’ attention.
- What data scientist skills are most sought out.
- How an entry level data scientist resume can present you in a favorable light.
- What clues to look for in the data scientist job description.
Looking for related resumes?
- Data Engineer Resumes
- Entry Level Data Analyst Resumes
- Tech Resumes
- SQL Developer Resumes
- Tableau Developer Resumes
Create a great data scientist resume outline to set yourself up for success
It’s easier to start a journey when you have a map. A data scientist resume outline serves the same purpose.
Here are the sections we recommend including on every data scientist resume:
- Resume Summary or Objective
These sections will help you showcase your background, as well as the knowledge you have in relevant fields.
Including both an Experience and Projects section will give the recruiter information he’s used to seeing in a reverse-chronological order - your experience - but it also allows you to highlight specific things you’re really proud of working on.
In similar fashion, having a formal Education section and a Certifications section provides you with additional opportunity to showcase knowledge gained. And this is especially helpful in the data science field where people can come from a variety of technical or economic fields and then get niche specialization in data science.
Finally, adding a Publications section can help you showcase articles you’ve written - and we don’t mean only publications in scientific journals. Since data scientists need to interact with a variety of audiences, it’s good to show you can explain ideas in a clear and efficient manner - and there’s no better proof of that than written pieces.
Still, you should use this as a general suggestion, not a rule set in stone. Both the sections, as well as the order in which these sections show up in your resume, is up to you.
How to choose an effective data scientist resume template?
There are a lot of options out there in the world of resume templates. A modern one will “jump” off the pile of applications and make sure yours actually gets read. A neat one will naturally guide the recruiter’s eye through the content and help them understand what you’re all about.
- Single column - that’s your best bet if you know the company you’re applying for uses an Applicant tracking system (ATS). A single column resume is easy to read and clear to understand.
- Double column - if you want to make a good impression and really distinguish yourself from other data scientist resumes, you can use this template. It adds some more space and let’s the text in your resume “breathe”. It’s a great choice for an entry-level data scientist resume, as you can add only your education, a couple of projects and passions, and already get a full page resume that packs a punch.
- Condensed - for senior data scientists, the main challenge is how to fit everything - rich experience, numerous skills and certifications - into one or two pages. This template will help you do just that. It’s also a great option if you’re looking for a more formal or concervative resume template.
- Modern - still want something that’s compact enough, but has that extra splash of color, an icon or two? Then the Modern template is the way to go. It’s tasteful, but also has some flare that will set you out from the rest.
While we can’t tell you which resume template will work for sure, we can give you the key rules of thumb you should consider:
- Choose a template that complements your content - look for something that works well with the amount of content you’ll put on the resume. Some templates look empty if you don’t have lots of experiences. Others will make lots of text look too crowded.
- Everything over 2 pages won’t get read - try to fit your resume on one page, two at most. Don’t be scared to cut out experience that’s not that relevant to the position you’re applying for.
- Avoid long text - no bullet point should be longer than 2 rows. Your resume is not the place for prose, save that for the cover letter.
- Choose fonts that look professional - we don’t feel like this needs to get said, but the examples we see online beg to differ. A resume is no place for playful fonts. Stick to something clean and professional, and avoid Comic Sans and Papyrus like the plague that they are.
- Add some color, but don’t overdo it - adding an accent color will make your resume look less like… well, a boring data scientist resume. The key is balance - adding a nice color combination will make you stand out, but adding anything more will make it the resume of a madman.
What makes a great data scientist resume header?
The first rule of a data scientist resume header is “first do no harm.” In other words, no unprofessional email or photo when it’s not allowed.
But beyond that, a resume header can actually add a lot. By quickly telling a recruiter who you are and giving them access to useful info about you on a personal site, you can make a strong first impression.
A data scientist resume header should have:
- Specific information about who you are (not just that you’re a data scientist but that you’re more junior, senior, a recent graduate, etc.)
- Contact information that makes you easy to get in touch with via phone or email
- No information that violates company or state laws (more about that below)
- A link to a personal Github or other page to show off data science work you’ve done.
The second example provides more relevant details like that Latisha is entry level and links to her github account, a nice touch which shows that she’s proud to show off her work.
Why you need a data scientist resume summary and what to put as resume objective
One of the inevitable questions most people ask when writing a resume is what to include, a summary or an objective. Or maybe both?
Resume summaries are a great way to share a condensed version of your professional (and personal) story.
A resume objective is great for an entry-level data scientist who wants to show their passion for the subject and to prove their motivation.
A summary is also great when you’ve transitioned into data science from another field. And the best resume summaries are catchy. A favorite summary we’ve seen started with “I am an architect that got into studying data science as kind of a weird mid-life crisis.” The recruiter will surely want to learn more!
The one mistake we see most often in resumes reads something like this:
If we can paraphrase President Kennedy, say not what the employer can do for you, but what you can do for the employer.
A clear objective clearly states what value you’ll provide the business with:
See the difference? This second applicant clearly states what they have to offer.
A data scientist resume objective or summary should:
- Show your motivation, why do you want to be a data scientist? Your passion can be just as important as your experience.
- Demonstrate your skills (at least in some basic ways, you’ll have more details in the rest of your resume)
- It should tell a story and capture the recruiter’s attention, including information about your long-term career goals if relevant
How to create an impactful data scientist experience resume section?
The experience section is the meat of your resume. It’s where all your hard work gets to shine.
To make it most impactful, you should follow a couple of key rules:
- Include only major and relevant positions - the 2-month stint as a salesman at your grandfather’s banana stand interests no one. But that job as a data engineer working on sales data for a national fruit reseller - that’s something the recruiter needs to see!
- Make it reverse-chronological - it’s the resume standard and it saves mental energy for the recruiter. So add your most recent positions first.
- Focus on impact rather than responsibilities - data mining, statistical analysis, and data visualization will be on pretty much any data scientists’ resume. The question is what was the impact of your work on the business. So explain that rather than just listing responsibilities.
The third point is so important that we want to illustrate this. Consider the following experience section:
This sure packs a punch! The person who held this position will know what they are talking about when it comes to data science. Now consider if they had only the responsibilities listed there:
It’s underwhelming and bland - and it’s the same person! So take a point to explain what were the results of your work. You sure have a lot to be proud of - show it.
Writing an entry level data science resume
Every fresher looking for their first job in data science will read this section and start thinking “well, I’m done, I don’t have any experience yet!”
Not so fast! If you think you don’t have any experience, then you are mistaken. Think about adding:
- Course projects that involved data science work - if you’ve gone through the effort of learning data science, you sure have practiced your skills on quite a few practical exercises. List them here. Just make sure you first include the new and exciting projects - no one wants to see the same tired Titanic Survivor project, so try to mix things up.
- Internships - no matter if it’s your uncle’s company or a university help gig, you probably learned lots, including keeping up with deadlines, working well with others and communicating data results to different audiences. Practical skills matter, even if they are soft skills.
- Volunteer work or side projects - if you don’t have practical experience, create some. There are tons of local SaaS startups that would benefit from logistic regression analysis to uncover their user activation points - help them out and use that as a practical example in your resume.
As you can see, there’s lots going on beyond traditional 9-to-5 steady job experience. And all of these will work well on your data scientist resume.
Creating a data scientist education section that shines on your resume
You’ve come a long way to become a data scientist. You’ve put in tons of hours reading O’Reilly textbooks, debugging Python scripts, and creating visualizations in Tableau.
So make all this hard work show on your resume.
For a stellar education section add information about:
- Your university and major - that bit is pretty obvious;
- Your GPA and final marks;
- Key courses relevant to the position you’re applying for;
- Any awards you received or societies you were part of.
Entry level data scientists should be especially diligent when presenting their education, while senior specialists can add a shorter format. Still, consider these two examples - one has everything a recruiter would be looking for, the other has a lot left out.
Don’t be afraid to expand your education section - done right, it can be the best asset of your data scientist resume.
What skills do data scientists need to have?
A data scientist position requires a unique set of skills that lets you ingest, transform, visualise and model datasets. They also need to communicate constantly with diverse stakeholder groups. So you’ll need to show a combination of technical skills and soft skills in order to make an impression.
First and foremost, you’ll need the skills to get the job done. In “Top 10 Big Data Skills to Get Big Data Jobs” Amit Verma presents a comprehensive list of languages and systems data scientists should be able to work with, including:
Make sure you include only things that you know well enough to start working with tomorrow. There’s no point in inflating expectations and then missing the mark.
What about soft skills?
Knowing the technical stuff often doesn’t cut it. As a data scientist’s role in the company is key, you will need to show you can handle the responsibility and deliver quality work.KDnuggets lists a few important soft skills, and we’ve added a couple more:
The world of data is complex and you should demonstrate you can navigate through it, but also help others orient themselves in it. Make sure you cover this, especially for more senior positions where presenting to managers is everyday work.
How to add skills from the data scientist job description?
Knowing what you’re good at is only part of the equation.The other part is making sure you can provide what the employer is looking for. So don’t fill your skills resume section in a vacuum - make sure you compare it with the data scientist job description.
Here’s a simple step-by-step process to do that:
- List your skills, both hard and soft.
- Check out the job offer and highlight all skills mentioned there.
- Compare the two lists and add all the skills you have and were mentioned by the job description.
- Add a couple more - the ones you think are your differentiating strengths.
Now let’s try applying those steps to a section taken from a real posting for a data scientist job:
“At Lockheed Martin Rotary and Mission Systems, Cyber Solutions, we are driven by innovation and integrity. We believe that by applying the highest standards of business ethics and visionary thinking, everything is within our reach – and yours as Lockheed Martin employee. Lockheed Martin values your skills, training and education. Come and experience your future!”
The rest of the job description is fairly clear when it comes to listing the precise skills required, but from this introduction you can pick up some other key elements to emphasize in your data scientist resume. Let’s break down what these mean:
Innovation: The company sees this as a core value, try and emphasize any times you applied your data science skills in unique ways to solve new problems.
Integrity: Ask yourself how you can show you have integrity. This could be emphasizing being a Boy Scout (cliche but it works), that you volunteer, or something else.
Business ethics: Maybe you randomly took a business ethics course in university? If you spot this, it might be worth mentioning in your education section.
Visionary thinking: Even if you’ve never done anything visionary, you can emphasize looking towards the future of AI and data science in areas like your resume summary or objective.
Adding resume elements that emphasize what’s mentioned in the job description is a subtle but powerful way to make your resume stand out.
Top 5 skills on data scientist resumes vs job offers
We analyzed over 100,000 resumes and job offers on Indeed.com to discover what skills were most in demand for data scientists. This chart breaks down which skills were mentioned most often in resumes and which were mentioned most often in job offers. Looking at the difference, you can spot which skills will make the greatest difference for you.
Listing data science projects on your resume
The more relevant information you can include on your resume, the better. So adding a section that highlights data science projects you’ve worked on will help recruiters get additional experience data points about you.
So if you have worked on specific projects, be it during your education or as side projects, list them in a dedicated Projects section. You could also add here key projects you’ve worked on in your past work positions.
Whatever you do, make sure to not only explain what the project was about, but also show what the impact of your work has been.
The first version will leave a recruiter feeling “Meh!” and the second one will get them to say “Wow!” Needless to say, you’re aiming for the latter.
Adding publications on your resume
Looking at other potential sections you can include in your resume, a Publications section might be an interesting addition.
The reason is simple - a good data scientist is not just a numbers person. They need to be a clear communicator, too.
And a Publications section will highlight just that - your ability to clearly communicate complex ideas.
When a person of science thinks about publications, they immediately default to research papers published in reputed peer-reviewed journals. Don’t do that. A Publications section can include links to your own blog or some guest posts you’ve written online - for example, articles explaining a specific function on your university’s website or a case study for your company’s website.
Add data science certifications on your resume
Since data science is a relatively new field, it’s common for professionals to come into data science from different fields. In this case you can shorten your education section and expand on additional courses you took - that’s where the certification section comes in.
Make sure you follow a few rules when presenting certifications on your resume:
- Make them stand out - don’t bury your certifications in another resume section, give them their own.
- Add any capstone projects you worked on - certifications usually make you show what you learned in practice. Mention your capstone and other projects you’ve worked on.
- Show how fast you made it - if you completed the certification course quickly, you can mention it on your resume. It shows dedication and motivation to learn.
Should you add more personality to your resume?
You’re a serious person, you’re applying for a position of great responsibility.
We get it.
But after all, people work with people. So showing what lies beyond your skills, knowledge, and experience matters.
So it’s more than logical to include elements that demonstrate your personality in your resume. Think of:
- Your passions and interests outside of work;
- Your favorite books;
- What your typical day looks like.
These are powerful differentiators that will make your resume more than a data scientist profile. It will help recruiters determine you’re a good culture fit for the company and it will make hiring managers excited to meet you.
Such elements also provide great discussion points during an interview - they will help recruiters know how to approach you and make the conversation easier. It’s also something that will make the interview process more natural and put you at ease.
Key takeaways: what makes a great data scientist resume?
To sum it all up, a great data scientist resume needs to tick a few different boxes:
- Showing you know your stuff by presenting relevant education and certificates;
- Demonstrating practical knowledge with experience and projects you’ve worked on;
- Showing how your skills align with the requirements in the job description;
- Adding a pinch of personality with additional sections.
If you manage to do all that and still keep your resume to two pages max, then congratulations - you’ve got a solid foundation for earning your next dream job as a data scientist!