The Subtle Art of Crafting Your Data Science Resume

You never get a second chance to make a first impression. It’s an expression that many of us are familiar with. It especially stands true when it comes to job searching, and it goes without saying that your resume almost always inspires that first impression for a prospective employer. We recently held a career development workshop for our students via Zoom, “Crafting your Data Science Resume”, with Adrian Schofield, a recruiter and coach from Hudson Modeling and Coaching, on how to create an effective resume.

How important is your resume? What should you include? What are some tips for creating a great resume? Adrian kicked off the session by addressing these questions. “A recruiter takes about 7.4 seconds to look at a resume,” he emphasized. Ultimately, you have less than ten seconds to make a good and impactful first impression. “According to the US Bureau of Labor Statistics, opportunities for data scientists are expected to grow by around 28–30%, which is about 11.5 million new jobs by 2026,” Adrian stated. The point being that the resume has probably never been more important in the data science job market given the amount of competition on the horizon. Structurally, he stressed that a resume should include the following elements: Name and Contact Info, Career Objective and Professional/Personal Summary, Work Experience, Projects, Skills, Education, and Awards or Recognitions. He also highlighted the importance of utilizing a clear format and choosing (and customizing) the appropriate template for your particular resume.

Here are a few pointers he gave for addressing different sections of a given resume, which are detailed below:

Professional Summary

It can be a nice touch in some instances to include out of school (or work) hobbies or interests in your professional summary. For example, if you’re pursuing a role in leadership, and you have a hobby or interest that demonstrates this (for example, guiding others, mentoring, facilitating, managing others), adding these skills to your professional summary is likely to raise further curiosity in your resume.


Projects should include a story, meaning there should be a beginning, middle, and end, that gives a sense of what the project entailed. The beginning should give a general overview of the project and explain what the challenges were. The middle should provide details on the type of coding/technology and predictive models that were used/implemented and should clarify if the project went into production. Lastly, the conclusion should explain the outcome. Simply put: focus on what you did, how you did it, and the result.


Keep in mind the first person to review your resume will likely be a recruiter who probably doesn’t have the same level of technical expertise that you do. Thus it’s important to tailor the skills listed on your resume to the particular job description you’re applying to. It’s important to note that some companies use an applicant tracking system (ATS) that automatically screens resumes by comparing keywords listed on your resume with that on the job description. If your resume is lacking these, you may be automatically rejected.


The education section should include all information about your academic background, including any secondary degrees and potentially your GPA.

Awards and Recognitions

This section should include any accomplishments that you’ve had whether that be at school, work, or an internship i.e. competitions, GitHub awards, hackathons, etc.

*General Tip

We’ve all heard of the one-page rule. However, resumes don’t necessarily have to be held to a single page. Two pages can be just fine so long as the information included is pertinent and relevant to the job you’re applying for.

Overall, it was both an informative and fun session. For those interested in booking a career coaching session with Adrian Schofield, please contact Francheska Jimenez, Program Administrator for Academic and Student Affairs at

By Ashley C. McDonald



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Official account of the Center for Data Science at NYU, home of the Undergraduate, Master’s, and Ph.D. programs in Data Science.