Data science is hot. It’s trendy. And it pays well.
According to Glassdoor, “Data Scientist” is among top-3 jobs in 2021, with a median base salary around $114,000.
Over the six years the number of data scientist jobs increased by 650%!
And surely, when there’s a great offer, there are also a lot of takers.
Do you want an impeccable data science resume that gets you ahead of the curve?We helped hundreds of data scientists find jobs in some of the most exciting tech companies in the world. Let’s help you too.
This guide will teach you:
- How to create a data science resume that puts you on top of the hiring list
- What data scientist skills are most sought out in 2021
- What clues to look for in the data scientist job description.
- How to write an entry level data scientist resume that presents you in a favorable light.
Looking for related resumes?
Data Scientist Resume Example
How to write a job-winning Data Scientist Resume
Utilize competence triggers.
Competency triggers are popular in interviews, however they are of particular importance when it comes to data science resumes.
- Competence triggers are behaviors or characteristics that cause someone to be perceived as competent.
A Github page, a Kaggle profile, a StackExchange or Quora profile, and a technical blog with relevant projects are some key competence triggers that will help you stand out in a BIG way.
In this guide will go throughevery section of your Data Science resume and make sure it organically merges competence triggers with your experience, skills, and projects.
Format, format, format.
A recruiter typically gets hundreds applications for every job they post online. Some of these data science resumes will never be read. Why?
Recruiters don’t have time to look for hidden gems when other candidates make sure you see their winning qualities right off the bat.
- Make your data science resume easy to read. This tip is a bit on the nose, but if you want to make sure that recruiters read your resume, make it… readable.
With a clever use of white space,eligible fonts,color, and headers you can make the process of reading your resume not only simple, but enjoyable.
Scroll back to the header to see a data science resume that hits all the right buttons when it comes to both efficiency and legibility in the header.
- Control what gets noticed first.
Lots of people are switching to data science from different backgrounds. Every one of them thinks that their previous experience makes them a unique fit for the job.
While having a unique relevant background is certainly a benefit, recruiters first need to establish that you are a great data science candidate.
With a clever layout, headers, and bold text you can GUIDE recruiter’s eyes.
The results will be much better if they read first about your data science achievements and core results, and only then about your five years' socio-economic background.
Below we’ll explore how to control a recruiter's perception and emphasize the most crucial information in every section of your resume.
Use custom sections to highlight project experience.
Project-driven experience is highly valuable in the data science field today.
Many candidates completed learning projects and did some data-science related tasks, but finishing a complete project from start to finish paints you in a completely different light.
Make sure to usecustom sections such as “Projects”, “Accomplishments” and “Key Skills” to highlight your ability to complete data-science projects in the top third of your resume.
What are the top sections of a job-winning Data Science resume
- A properly formatted header with relevant and convincing profiles
- Attention-grabbing, concise Summary tailored to the job you want
- The Experience section, concise and clear to help make your resume a high priority among recruiters
- Technical Skills section with the most relevant and crucial skills for the job
- Custom-tailored sections to strengthen your resume
Data Scientist Resume Header: First Impressions Matter
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.
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.
Data Science Resume Summary: Creating A Perfect Match In Two Sentences
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:
Helped USA mining companies to improve efficiency using custom data model
If we can paraphrase President Kennedy, say not what the employer can do for you, but what you can do for the employer.
Your data scientist summary should clearly state what value you’ll provide the business with:
Adapted the core physics-based excavation model of a $50MM company for providing customized energy saving recommendations for Top 10 mining companies in the USA.
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
Check outour detailed summary guide with 30+ professional summary examples to learn more!
Data Scientist Resume Experience Section: The Most Impact In The Fewest Words
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 itreverse-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:
Senior Data ScientistDNB BankCompany Description
Designed and implemented models for loan success factors, achieving a 20% improvement of approval decision time.
Spearheaded complete database restructuring of the Financial Aid Database used across 16 different countries.
Coordinated a team of 20 data scientists working on 6 different projects for insurance, finance, marketing, and security departments.
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:
Data ScientistDNB BankCompany Description
Created and presented models for loan success factors.
Did database manipulation of the Financial Aid Database.
Coordinated a team of data scientists.
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.
How to write an entry level data science resume
Everygraduate 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.
Data Scientist Education Section: The Best Bits
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 astellar 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.
BS, Data ScienceUC Berkeley
Data Science Major Foundational courses in Mathematics and Computing
BS, Data ScienceUC Berkeley
Took additional courses in Big Data Ecosystems and Data Visualisation 201
Won 3rd place in the Student City Datathon with a project on parking data modelling
President of the STEM Diversity Society for 2 consecutive semesters
Don’t be afraid to expand your education section - done right, it can be the best asset of your data scientist resume.
Data Scientists Skills Section: Which Ones Are THE Ones?
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:
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.
- Communication skills and ability to boil down complex subjects to simple terms.
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 a 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.
How to Add Publications on Your Data Science 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.
How to 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.
Top 20 Data Scientist certifications you can take:
- Google Certified Professional Data Engineer (GCP)
- Cornell Data Analytics Certification
- Certified Analytics Professional (CAP)
- Professional Certificate in Data Science from Harvard University
- Applied AI with DeepLearning, IBM Watson IoT Data Science Certificate
- Cloudera Certified Professional: CCP Data Engineer
- Data Science Council of America (DASCA)
- Dell EMC Data Scientist Associate (EMCDSA)
- Dell EMC Data Scientist Advanced Analytics Specialist (EMCDS)
- HDP Data Science
- Microsoft MCSE: Data Management and Analytics
- Microsoft Certified Azure Data Scientist Associate
- Microsoft Professional Program in Data Science
- SAS Certified Advanced Analytics Professional
- SAS Certified Big Data Professional
- SAS Certified Data Scientist
- IBM Data Science Professional Certificate (Coursera)
- Data Science and Statistics Certification by MIT (edX)
- Machine Learning Certification by Stanford University (Coursera)
- Data Science Certification from John Hopkins (Coursera)
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.
Bonus Section: Tips For Specific Data Science Resume Formats (With Examples and Templates)
Senior Data Scientist Resume
- Align with business problems. In many cases, a senior data scientist will become the first data science expert in the company. Businesses might not know details about your expertise, but they surely want to know how you helped other businesses in the past.Target your resume toward the job.
Make sure to frame your experience, projects, and summary sections to be as goal-driven as possible, using the challenge-workflow-outcome framework.
- Show that you can spearhead projects from the ground-up. Earn bonus points by demonstrating your ability to wrap up entire projects from start to finish. Describe entire pipelines and workflows that you used to achieve results and utilize Projects section to present your experience as Project-driven.
- Demonstrate your ability to communicate with business stakeholders. Show how you cooperated with BI analysts, technicians, and executives when describing your work experience. When you demonstrate that you can fit well into the company and work together to accomplish tasks, that goes a long way.
Entry-level Data Scientist Resume
- Showcase data science and analytics projects. Use custom sections such as Projects and Personal Projects to highlight finished projects related to data science.Often these are the first things recruiters will be looknigfor in junior data science resumes, so make sure to put them before other sections. Use hybrid resume format, if needed.
- Emphasize any background that indicates a strong foundation in either software engineering or math/statistics. Plan your resume layout to put the most relevant data science experience above anything else. For example, put the relevant coursework above unrelated Experience and data science-related Projects above anything else.Do not make the recruiter search for it.
Python Data Scientist Resume
Linkedin profiles study indicates that Python is the most used language in data science in 2021.
Therefore, Python knowledge is in high demand and the demand is even more prominent in some jobs. This is how you can maximize the potential of your Python Data Scientist resume:
- Mention how you use Python in your projects. Don’t just insert a Python word in every other section. Demonstrate how you used Python within your projects. For example, after describing a project in your Experience section, add a line that contains the tech stack that you used for this project, including Python.
- Link to Github Python projects or portfolio. If you are confident that tech recruiters will be looking for Python developers, make sure to guide their attention to your Python data-science projects first.
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!