According to Glassdoor, “Data Scientist” tops the list of the best jobs in 2020, 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.
“I was ready to answer questions about real life work, how to deal with complicated situations, how to deal with new data, how to do a data science workflow, how to explain hard concepts to managers and more.”
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.
Whichever you choose depends on your years of experience, as well as whether you’re coming from the same or adjacent industry.
Reverse chronological resumes are best for experienced individuals who stuck in their niche for a while.
Functional resumes are used by less experienced job seekers or career changers. Note that it’s not a format that recruiters prefer, as most are used to the classic chronological alignment.
Hybrid resumes are great for both experienced and entry-level candidates, as well as people coming from adjacent industries. They combine the best of both worlds.
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.
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. Use 10’’resume margins - that’s default for a great resume design;
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.
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.
+359 88 888 8888
Entry-level data scientist
+359 88 888 8888
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.
PRO TIPSome companies, states, and countries have non-discrimination policies about what kind of information can be included on your resume. This might include a photo (which is often included in a resume header and might be on personal web pages you link to). You can always email the company’s HR department to ask about their policies before you apply.
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 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:
An entry-level data scientist who wants to expand into the big data field and build deeper engineering capabilities.
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:
An entry-level data scientist who takes pride in building models that translate data points into business insights. Used my skills to win the Student City Datathon challenge, now eager to apply the same knowledge to real-world business problems..
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.
Every graduate 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.
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.
A data scientist position requires a unique set of skills that lets you ingest, transform, visualize 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:
Top Data Scientist technical skills
Programming languages including Python, Java, C, and Scala
Quantitative and statistical analysis tools like SAS, SPSS, and R
Apache Hadoop and its components like Hive, Pig, HDFS, HBase, and MapReduce
NoSQL databases including Couchbase and MongoDB
Data visualization tools like QlikView and Tableau
Data mining tools like Rapid Miner, Apache Mahout, and KNIME
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:
Data Scientist soft skill examples
Ability to work well with others as well as individually.
Critical thinking and problem-solving skills.
Adaptability and propensity to learn new coding languages and programs.
Understanding of general business processes, as well as tangentially related fields such as marketing, HR, cyber security, transportation, or customer service.