This is the era of the geek, so let your flag fly! If you love crunching numbers, count yourself lucky: The Harvard Business Review named data scientist as the sexiest job of the 21st century. In an explosive labor market where big dreams can come true, taking a deliberate, analytical approach to develop your data analyst resume is the best way to increase your odds or even get multiple job offers.
Resume.io can help you put it all together to interview-winning effect. Our job search resources include more than 350 occupation-specific resume examples paired with writing guides.
With a time investment of about 20 minutes, this guide — backed by a data scientist resume example you can customize for your own use —will take you through the process step by step. You’ll learn how to:
First, let’s investigate the data science industry.
Businesses are snapping up people with your skills in record numbers. What’s behind these seemingly infinite choices of positions? Why would Harvard call data science sexy?
The field reaches into all our lives, almost every day and you are on the forefront of making that data meaningful.
Big data and data analysis spending is forecast to have a compound annual growth rate of 13.2 percent from 2018 to 2022. That means that by 2022, revenue would reach $274.3 billion, IDC believes.
In our technology-driven world, companies are gathering large caches of information about human behavior, demographics, and any other category that can be quantified. That information is useless on its own. That’s where you come in. As a data scientist, you take that data, analyze and interpret it, so that it can be used to make decisions about everything from which color to make a product to where the biggest need for a government program lies. Data scientists work with unstructured data and their modeling techniques may include machine learning and deep learning protocols.
Many industries capitalize on big data. Which ones are making the most of it right now? Here are the top verticals where Data Science Central believes data scientists can use their skills best and earn the highest salaries:
The GeeksforGeeks list adds even more potential employer sectors:
Each of these industries uses big data to inform decision-making and solve problems, but each has different needs and goals. With that variety, it will be in your best interest to focus your resume as specifically as you can to each of your areas of interest.
The average annual base salary for data scientists is $ $97,483, according to Payscale. The top 10% of earners make $136,000 and the bottom 10% earn $69,000..
You need to be able to explain in your resume what value you will bring to a business with your analyses. Businesses are looking for ways to streamline processes, save money, sell more, improve efficiency, get ahead of trends, identify and refine their target audience, recruit appropriate talent, and use quantifiable evidence to make and test decisions. You can do that, but first you have to sell yourself.
What virtually all resumes have in common, no matter what the profession, is their barebones structure, as follows:
Step by step, we’ll look at each of these essential elements shortly from a data scientist’s standpoint. But first, here are some general considerations.
Your resume should fit on a single page. The more experience and skills you have, the more selective you will need to be in determining the most directly relevant content for your data scientist resume. And, as we’ll continue emphasizing, your resume should always be customized to match the requirements of every different job you apply for.
That brings us to the first big challenge for many job seekers — passing through the applicant tracking systems (ATS). But as a data scientist, you should have little trouble. These systems use algorithms to rank your resume based on employers’ requirements. Resumes that don’t rank high enough end up on the slush pile.
There are hundreds of ATS programs on the market, so there is no single way to ensure you will beat one, but as a data scientist, you may have an advantage because you know how to analyze information. Your first step is to do just that. Look at each job description (researching the employer also helps a ton) and decide which listed skills and attributes are most important to that employer. Then, make sure you use those skills and attributes in your resume.
Here are some facts to help you ATS-proof your resume:
Unless your career path is taking a new turn or is geared to consulting roles as an independent contractor, the most commonly used chronological resume format should be suitable. If you’ve worked primarily as an employee, this is the simplest way to organize your achievements in bullet-point highlights below dated employer headings. It’s also what recruiters prefer.
Consider alternative resume formats if you want to emphasize specialized skills, clients or projects, rather than where you’ve worked. In that case, a functional or hybrid (combination) resume format might be more suitable. Later on, we'll discuss the reasons why some data scientists might prefer to showcase their achievements in a "projects" section instead of the conventional employment history.
Having trouble with putting your data scientist CV together? Get inspired by these IT resume samples below:
Now that you have an overview, let’s jump into crafting your resume.
Choose a resume header design that will catch the reader’s attention and make a lasting impression. It also makes the document look more inviting to read, while your name, occupation and contact information stand out. Strengthen the impact by making your resume and cover letter match visually with the same personally branded design elements.
Your resume summary — or, if you prefer, your profile or personal statement — briefly tells recruiters who you are and what you find most important about your career thus far. In this short narrative — no more than three to five sentences — you have the opportunity to showcase your professional personality and attributes while highlighting one or two of your biggest successes.
This is the place to put in adjectives that describe your workplace demeanor and explain why you are the best candidate for the job. If you are tempted to be modest, resist! You should be proud of your accomplishments, but steer clear of overstating. If you are looking for your first full-time job, use any projects you have completed either at university or because as a data geek you do this stuff for fun.
Each summary should reflect the job for which you are applying. You don’t have to rewrite a completely new version for every position, but be sure to integrate the keywords that your analysis found most important to give your ATS ranking a boost.
Although this section appears at the top of your resume, you may want to write it last. That may depend on whether laconic creative writing is among your strengths. After all, it is called a summary so it may help to have all the data in front of you first.
See the data scientist summary from our resume example below.
Accomplished Data Scientist with a passion for delivering valuable data through analytical functions and data retrieval methods. Committed to helping companies advance by developing strategic plans based on predictive modeling and findings. Proven track record of analyzing complex data sets and serving as a solid, reliable advisor.
The employment history is the meat of your data scientist resume: It gives recruiters the roadmap of your career progress. Think of this section in the same way you think of putting together chunks of data to form a big picture. Each job or data science project is a stepping stone to the next.
The key idea here is to show recruiters what you have brought to each job, how you have grown, and what steps you are ready to take next. Many data scientists begin their careers as software developers, data engineers, business analyst or data analysts, so if you’re trying to make the leap to data scientist, be sure to show a clear pattern of growth in responsibility and knowledge. Begin by thinking about all the skills you need to be a successful data scientist. You can think of what Innoarchitech calls the four pillars of data science:
The Harvard Business Review describes a data scientist as a combination of data hacker, analyst, communicator, and trusted advisor. Being expert in all these areas makes you a true unicorn.
Remember to name the “Three Vs” that you dealt with — volume, velocity, and variety — when describing your data projects.
Go heavy on the details in this section. At the same time, avoid unnecessarily wordy sentences and cut down on “auxiliary grammar” that can be shortened. This is one way to demonstrate that you track your own achievements and growth. Use strong action verbs as you write.
Here are some sample phrases to get you started:
When you write your work experience descriptions, try to anticipate questions recruiters may ask you, and include some answers. Potential questions may include those from the following categories:
If you can organically incorporate answers to these questions, or examples of how you have used these ideas in your employment history descriptions, recruiters are more likely to grant you an interview.
You may have completed many projects that were not related to your employment or you may have been working as an independent contractor. If either of those is the case for you, consider organizing your experience by projects. That way, you can offer recruiters more details about your biggest achievements. You should still follow the same guidelines in the employment history section:
This format for organizing your work history can also help if you have any gaps in employment. These gaps are less obvious, and more explainable, if you have been working as a contractor or on a single-project basis.
This section is also a great way to include open-source contributions and blogs or social media content that relates to your career.
Below is a data scientist employment history sample you can modify.
Data Scientist at Viacom, New York
June 2016 — September 2022
Junior Data Scientist at Macy's, New York
July 2014 — May 2016
Data science is a fairly new area, so you probably don’t have a degree in it, although certification programs and “bootcamps” are popping up to fill the void. If you have completed one, that’s great, but you can get a job as a data scientist without one. The most sort-after degrees for recruiters seeking data scientists are:
If you have a minor in any of these subjects, you should also include that when you list your education. Many data scientists have master’s degrees, which focus their field of expertise, but a master’s degree is not a requirement to get hired as a data scientist, especially if you have a background in the subjects above. If you want to strengthen your resume or fill in skill gaps, consider a certification program or further studies. Other STEM degrees, such as biotechnology or engineering, may also lead to a career as a data scientist.
Below is the education section from our data scientist resume example.
Bachelor of Computer Science, Fordham University, New York
September 2010 — May 2014
The skills section of your CV is an at-a-glance list of the talents you have that match the job you are seeking. It should include from five to 10 of your top abilities.
Now’s the time to brainstorm a list of every program, statistical model, branch of math, etc. that you know. These are the hard skills you need to do your job. Next, think about your people skills and other soft skills such as communication, organization, and time management required to be a productive worker. Turn this into a master list that you can add to as you grow as a data scientist and search for new challenges.
This is the easiest section to individualize for each job, but it shouldn’t be the only one you alter. Make sure you consider each job description and target those specific requirements when you customize your resume.
This section is also a great place to add those rare keywords that will distinguish your resume and help you beat the ATS. Skills can be thought of in these three categories:
As you build your skills section, choose higher-level skills over lower-level skills. You don’t need to list any necessary skills if you have defining and distinguishing technical skills. Don’t neglect soft skills, however. As a data scientist your analytical ability extends to communicating your findings and making the case for your solutions.
Check out the data scientist CV sample for the skills section below.
As a data scientist, you know that the message is more important than an ornate design. Use this philosophy to develop the look of your resume. This is a visual representation of your professional personality, so be sure to reflect that.
Check for typos, save your resume as a PDF file, and scan for formatting errors. Even tech-savvy professionals can slip up. Don’t be one of them!
The main idea is to keep your resume document legible. Recruiters will spend only seconds scanning for information and getting an impression of who you are. If your resume features big blocks of type or a confusing layout, it is likely to end up in the garbage. Here are a few rules of thumb:
Start with one of the field-tested resume templates from Resume.io's four style categories: Simple, Creative, Modern, or Professional. If you choose to modify the design elements or formatting in any way, make sure you don’t overdo the color or change the font to one that is harder to read.
Use resume.io, the resume builder-tool, and its expertly designed templates to elevate your resume. Get started today!