How to Make Your Data Science Resume Stand Out

UW Online Collaboratives ● February 16, 2017

one cartoon resume that is lit up and standing out from all the others

When it comes to landing the data science job of your dreams, you only get one opportunity to make a first impression. You need to make it count.

How can you do that? It’s simple: A succinct and targeted data science resume and cover letter. When written effectively, both documents set you apart from other candidates and convey exactly what you can offer and why.

Polish your data science resume and cover letter using these tips so you stand out from your competitors.

6 Sections to Include in Your Data Science Resume

Writing a resume can be a daunting task, especially if you don’t know where to begin. That’s why we compiled these six components that every data science resume should include:

1. Contact information

You know to include your full name, credentials, address, phone number, and email address. If you have a professional presence on LinkedIn, Twitter, GitHub, or another social media site, consider providing links to those as well. Make it as easy as possible for hiring managers to get in touch with you.

2. Professional summary

Your professional summary should be no more than three or four sentences stating the following:

Years of experience
Primary areas of data science expertise (e.g., data mining, data warehousing, or data visualization)
Type of position you seek (e.g., data analyst, data scientist, or data engineer)
Industry in which you hope to apply your data science knowledge (e.g., healthcare, retail, government, or finance)

The professional summary should tell your story—where you’ve been and where you’re headed next. Think of it as your elevator speech—that is, the way in which you’d describe yourself and your abilities in 30 seconds or less. You’ll likely have different versions of this speech depending on the type of data science position for which you’re applying as well as the industry. This is your chance to grab a hiring manager’s attention and encourage him or her to keep reading, so make it worthwhile.

3. Core competencies

This section should include a bullet-point list of your data science strengths (e.g., statistical analysis, data interpretation, and communication) and explain generals tasks you’ve completed to achieve each competency. For example, with data communication, perhaps you could include these tasks: Write, format, and present technical prose; facilitate data-informed discussions; and help non-technical professionals act on data findings.

Refer to data science course outlines to help build this portion of your resume. That’s because most data science course descriptions also include core competencies and learning objectives that you can use as a foundation.

4. Education

List each degree and institution as well as the date of graduation. A Master of Science in Data Science proves to employers that you have the wide range of knowledge and skills needed to do any data science job. Consider listing any courses that are particularly relevant to the job for which you’re applying. For example, if you’re hoping to land a data scientist job in biomedical research, consider listing any data science ethics courses in which you applied various analyses using an ethical framework. Tie your education experience as much as possible to the job you seek.

5. Technical expertise and certifications

This section should include a bullet-point list of specific data science skills you’ve honed as well as tools with which you’ve worked. If you earned a data science master’s degree, list that you have hands-on experience with data science tools such as SQL Server and Tableau and languages such as R and Python. Don’t list tools for which you only have a theoretical knowledge. Hiring managers want to know that you have experience working with these tools and will likely ask you for examples during an in-person interview.

6. Work experience

Don’t worry about keeping your resume to one page—that’s an outdated notion. If you’re a mid-level or above professional, you’ll likely need two pages to list all work experience. And never skip significant job experiences, even if they’re unrelated to the job to which you are applying. A gap of time between jobs is a red flag to hiring managers. For unrelated job experiences, don’t include as much detail on your resume as you would for other jobs.

Tailor each job description to the one you’re applying for. For example, if the position you hope to land requires an in-depth knowledge of SQL, tailor your previous job descriptions to highlight your use of SQL.

Also focus on key accomplishments in each previous position by listing a select group of three to five projects and briefly describing your role in each project’s success. Use action words (e.g., created, managed, coordinated, or led) to describe your participation.

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Make Your Data Science Resume Keyword-Friendly for Hiring Managers

A strong data science resume also incorporates the keywords for which hiring managers are searching. This will vary from job to job, and a good rule of thumb is to incorporate as many of the same terms and phrases used in the job posting directly into your resume.

You’ll also want to incorporate as many general data science keywords as possible. For example, Towards Data Science lists these must-have resume skills and keywords for data science that you could reference if it makes sense to do so. Don’t force yourself to include these terms. Use this list as a point of reference, and be cognizant of it as you construct your skills, competencies, and previous job descriptions.

4 Things to Consider When Writing Your Data Science Cover Letter

Follow these four tips when constructing your cover letter:

Personalize your love for data science and why you want to work in this field. Tell a brief story about why you love data and what you hope to accomplish in the data science role that you seek.
Explain why you want to work for that specific company. Look at the company’s “about us” page, and respond directly to the values the company promotes and why these values are important to you as well.
Expand on projects listed in your resume. What skills did you hone, and what did you learn that you can apply going forward in a future data science position?
Remember the unrelated job experiences discussed above? The cover letter is your chance to highlight those experiences and explain how skills you gained in a different field will transfer to this job. This is what will make you truly stand out as a candidate.

3 Bonus Tips to Boost Your Application

Following are three other tips to consider:

Keep the layout simple. Use a straightforward layout and font for your resume and cover letter. Let the content do the talking. And never go below an 11.5 font size.
Proofread for grammar and punctuation. Data science is all about the details, and you don’t want to ruin your chances of landing an interview by misspelling a word or forgetting a crucial punctuation mark.
Invite feedback from a friend or colleague. Are there areas for improvement? Is the resume clear and succinct? Don’t hesitate to look at other data science professionals’ resumes as well. This includes LinkedIn profiles—especially those profiles of individuals who already work for the company to which you’re applying. What language do they use, and what skills do they promote? Use this as a guide for developing your own stellar and unique resume and cover letter.

Considering a Master of Science in Data Science? University of Wisconsin offers this degree online through six campuses. Learn more about the program and which UW campuses offer the degree here.