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How to Structure a Data Science Resume 

Caroline BantonUpdated Oct 13, 20217 min

How to Structure a Data Science Resume 

Updated Oct 13, 20217 min
How to Structure a Data Science Resume 

How to Structure a Data Science Resume 

Caroline BantonUpdated Oct 13, 20217 min

How to Structure a Data Science Resume 

Updated Oct 13, 20217 min
How to Structure a Data Science Resume 

Congratulations! You’ve hit the jackpot. The job platform Glassdoor ranked data scientist as one of the top three jobs in the United States four years in a row. But that’s not all. There is a huge demand for data scientists, and the Bureau of Labor Statistics expects close to a 15% increase in data scientist employment through 2029.

Now that you are excited and motivated to launch your data scientist job search, start out with a bang. Create the best resume you can so that you can target the job of your dreams. Here’s how to impress the Google’s and Amazon’s of the world (and all of the other incredible tech companies) and grab a top spot in one of the most dynamic fields out there.

We’ll show you how to create a data scientist resume that will sail through even the most discerning of applicant tracking systems. We’ll show you how to turn your computer and data job history into a compelling story that no self-respecting hiring manager can ignore. We’ll show you what to include, what to exclude, and how to stand out from all the other data dudes!

Lastly, we’ll show you an example of a finished product—a data scientist resume to die for. Ready? Then, let’s dive in.

First, Do Your Research

Research Google’s hiring practices, and you’ll find a lot of content about “Googleyness,” a certain group of characteristics that Google looks for in its candidates. Similarly, research Amazon and the phrase “innovative mavericks” jumps out at you. Through research, you can find out what type of person the company is looking for, and what you will be expected to do in your data scientist role.

Research, then, is the first step in crafting your resume. The job description is the first place to go to find out what to highlight on your resume. Then, read the content on the company website. If the company is well-known, do a quick online search and see what articles come up both in terms of hiring and in terms of their products. The more knowledge you have of the company, the better you will perform if you land an interview, so consider this time well spent.

Try your hand at social sleuthing. Chances are there is someone hanging out in a chat room or platform you visit who might be willing to give you the low down on data scientists in the organization. Try Quora, or go to the company’s LinkedIn page and reach out to a couple of employees. Another option is to call your HR contact and ask them to recommend someone who you could chat with.

Tailor your resume for each job that you apply to because each job and company will be different. So, initial research should be done for any job before you tweak your basic resume, draft your cover letter, and apply.

What to Include in a Data Science Resume

The job description and your research will dictate the nuances of what to include in terms of skills and experience. However, there are other key things to include, and there are resume formats that are better suited to data scientists. The elements to include are your personal information, an objective, your relevant experience, and your education and certifications. Let’s look at formatting first.

Simple Formating

You know all about data right? It is best when presented in a way that is visually appealing and easy to digest. You might be thinking graphics and illustrations ... don’t. The best resume for a data scientist is one page long with bullet points that showcase key skills. The “clean” template shown here is a good choice among resume templates.

The most important elements should be clear and should stand out. The clean template lists your skills in a separate column, so the reader’s eye is immediately drawn to them. Your technical skills should hopefully correspond to those listed in the job description. Don’t include your soft skills, for example, leadership or customer management. These are better discussed in an interview.

Personal Information

The first section of your resume will show your contact information. Include your LinkedIn URL, your Twitter handle, your address, and your phone number. If you have any advanced degrees or certifications, an MBA or Microsoft certification, add the letters “MBA” or the words “Microsoft Certified” after your name.

Resume Objective

Your resume objective is where you package yourself neatly and aim for maximum impact. Your resume objective should summarize you and your brand. It should be a strong statement about who you are, what value you can offer the company, and where you want to go professionally.

Here’s an example:

Motivated data scientist with strong math background and 4+ years of experience with startups. Used to solving challenging business problems with predictive modeling and data mining algorithms. Active in Python open source communities and passionate about its application to deep learning.

Let’s unpack the resume objective and explain each of the components. The opening uses the adjective “motivated.” Using a descriptive adjective describes your brand. Other examples of words you could use are “Results-driven,” “Analytical,” “User-oriented.”

The next part of the statement gives your years of experience. Data scientists should be knowledgeable in bleeding-edge technologies, so if you are an experienced scientist, keep your experience to “8+” years. Unfortunately, ageism is a problem, particularly in the computer science industry. The more current you are, the greater your value. You can be current and experienced, but there is no need to accentuate the latter beyond one or two past employers.

Next, the statement describes your unique specialty and your passion. Again, these should align with the job description and what you have found out about the employer through your research. The summary should highlight your primary technical skill(s) while the next section, your work experience, elaborates and adds to that statement.

Your Work History

A high-performing data scientist will have been involved in many projects, perhaps for more than one or two employers. They may have experience in many areas and used numerous data solutions. How do you convey all that to the employer? The answer is, you don’t.

If you do try to document everything you have ever done, you will fill your page with clutter that may be irrelevant to the job. Put your most recent work experience at the top of the resume and include a few bullet points describing projects that will have meaning and relevance to the target employer.

It’s better to focus on what you specialize in, and the areas where you have the most experience and knowledge. If you can align your experience with what the employer is looking for, fantastic! If not, don’t try to overstuff your resume.

Your work experience section needs to convey a few things:

  • The hard skills that you have developed and used in the past.

  • The results of your contributions and how they impacted the employer.

  • Continuity and progression—that you have stayed with a company for two years or more and have progressed in your career.

You need only list your last two employers if you are further along in your career and have been with a company for more than two years. Two employers are sufficient to highlight your skills, most recent experience, and your reliability as an employee. If you have not been with a company for more than two years, that could be a problem.

The COVID-19 pandemic has created gaps in many resumes, so recruiters are less alarmed by gaps than they were pre-pandemic. If you took a sabbatical, had to care for a relative, or there were other reasons why you took some time off, add a line in your resume to explain that. Don’t go into details because you want to draw attention to your accomplishments and not your time away.

Another way to address gaps is to only include the years that you were with a company and omit the months.

If you are a recent graduate with limited full-time work experience, add any summer internships, part-time, or volunteer work to this section relevant to data science. Perhaps you learned Python or another language in your spare time.

If you are a senior data scientist product manager with many years of experience, again, only show as many employers as it takes to respond to the job requirements and to show progression. Your last two or three positions should do it. If you have some experience from an old job that is relevant to the target, place the relevant skill in the “skills” section. That way, there is no need to add the date.

Don’t add soft skills, such as “leadership” or “communication,” in the skills section unless you had a significant leadership or management position. The hiring manager is looking for hard skills, and anything else takes up unnecessary space. If you led a particularly unique project, add this as a bullet point so that it could be discussed further in an interview if it is of interest to the interview panel.

Formatting the Work History Section

List your employers in chronological order starting with your current job. Use month/date format and add the name of the company. Use bullet points for each item.

As you list your professional experience and write each item, include keywords to ensure that a candidate tracking system will not reject your resume. Good keywords will be on the job description in the requirements section. If one of the requirements is “conduct advanced analytics” include “advanced analytics” in your experience if it is part of your work history. Also include words used in the data science industry. Here are some to get you started:

A/B testing; Artificial intelligence (AI); Augmented reality (AR); AWS; Azure; Big data; C++; Data analysis; Data analytics; Data mining; Data sets; Data visualization; GCP; Google analytics; Graph algorithms; Hadoop;  Hive; Java; Javascript; JSON; Lambda architectures; Machine learning; MapReduce; Natural language processing; NoSQL; NumPy; Pig; Python; Regression; Spark; SQL; Statistics; SciPy; Sci-kit Learn; Pandas; R; Scala; Virtual reality (VR)

Most importantly, use metrics to back up your experience. For example, don’t just say that you “Performed a random-forest model to improve monthly customer retention,” say “ Performed a random-forest model to improve monthly customer retention by 40%.”

Add metrics to your bullet points. You can use dollar amounts, percentages, or numbers to describe the results of data science projects you took on. For example, how much more revenue did save the company and over how many years? By what percentage did you increase the efficiency of a process or reduce errors? How many employees did you manage? How many calls per day did you field? With how many divisions did you collaborate on a project?

Tips for bullet points:

  • Keep them to no more than two lines in length.

  • For your most recent role, include up to five bullet points.

  • For previous roles, keep experience to three to five bullet points.

Example:

Data Engineer

Github, New York, NY

August 2018 to present

  • Built machine learning / quantitative models in Python using scikit-learn and Keras to predict customers most likely to leave the platform with an accuracy of 84%.

  • Implemented reporting in Tableau for problem-solving. Identified opportunities for cost savings resulting in $180,000 annual additional revenue.

  • Collaborated with marketing and product development and improved the efficiency of the company’s content marketing by 25% with trained machine learning models.

The Education Section

In the education section, list the schools you went to and the degrees you earned. Also, list any certifications you might have earned. These could also appear in the skills section if you prefer.

If you are an entry-level data scientist and don’t have any metrics to showcase real accomplishments with employers, it is fine to add your GPA if it is 3.6 or above. Also, add any relevant coursework to support your hands-on technical expertise.

If you are more advanced in your career, there is no need to add your GPA. Whether you add the date that you graduated is a point for discussion. Some applicant tracking software might screen out resumes that don’t include the graduation date. However, if you are a seasoned data scientist, and you graduated many years ago, it might be better not to add the year you graduated.

The Technical Skills Section

Data scientists often have specific technical skills, software proficiencies, or certifications. These should be listed in the section so that the employer can see them at a glance. If you have a lot of skills, optimize them by separating them out. The example below separates the skills by programming language, big data, and expertise. Remember, don’t include soft skills.

Example of a Technical Skills Section

Languages: Java, Python, R, Scala, C++, JavaScript

Big data: SQL, NoSQL systems, Hadoop, Spark, Azure

Areas of Expertise: Machine learning (ML), Natural Language Processing (NLP), graph algorithms, A/B testing

CertificationsData Science and Advanced Analytics Associate Certification (DECA-DS); Associate Certified Analytics Professional (aCAP)

If you have contributed at tradeshows or written articles or blogs. List them here or under a separate heading such as “Presentations” or “Published Articles.”

Final Checklist

Tailor your resume for each data science job that you apply for. The keywords and job requirements will be different for each employer.

  • Choose an uncluttered clean format with no distracting colors or images.

  • Clearly list your skills and education ideally separated out from the body of your resume in a separate column.

  • Update your LinkedIn profile to make sure the dates of your employment match your resume. If they don’t, this could be a red flag for recruiters, and they may not shortlist you.

  • Don’t list more than two or three employers.

  • Show career progression and explain any gaps in your work history.

  • Include keywords that you find in the job description. Use vocabulary common in the data science industry.

  • Exclude any irrelevant information, such as high school, GPAs, or school achievements if you are further along in your career.

  • Always have a third party proofread your resume.

Here's an example of a great data science resume (download the template here):

Data Science

Caroline Banton
Expert on career acceleration and business topics with vast experience writing for globally-recognized publications
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