Crafting a Standout Data Science Resume

Crafting a Standout Data Science Resume

Tips for Transforming Your Data Science Resume

Let’s face it, creating a data science resume isn’t high on your list of fun things to do. But your CV is a necessary part of applying for jobs in data science. This high-growth industry is booming, and it needs computer and information research scientists who are properly trained to handle big data.

Nearly every industry relies on individuals with knowledge of data visualization, analytics, and structure query language (SQL). Firms invest in employees with the skills to support marketing, finance, HR, and operations. This is one reason why the Bureau of Labor Statistics reports a “faster than average” growth rate of 16 percent through 2028 for these types of jobs.1 

As more industries turn to data scientists to assess risks and handle growing amounts of digital information, resumes are essential to get in the door and secure an interview. Show prospective companies your value by designing a data scientist resume that passes applicant tracking systems (ATS) while striking a chord with a recruiter or hiring manager.

Data Science Resume Best Practices

Forget about the boring CVs of the past where you list job duties. Instead, start with a data science resume template and personalize it to showcase your unique skills and proficiencies. Following these best practices can help you create a standout resume.

1. Choose a Data Science Resume Template

Data science jobs can be found in traditional organizations and innovative startups. Your data science resume template should fit the style of the company you’re applying to. You may use a conventional format for a business like IBM while you submit a more creative CV to companies like Google. Find free or low-cost templates by exploring:

  • Canva
  • Google Docs template
  • Microsoft Word template

Don’t forget to view data scientist resume examples to get a feel for the format, and be sure to choose a data science resume template that’s clean-looking and easy to follow.

2. Keep It Short and Visually Appealing

The best data scientist resumes are one page long with simple headers that guide the hiring manager through your CV. An eye-tracking study by Ladders finds, “Recruiters in our study spent more time focusing on job titles than on any other element.”2 Make your resume appealing by:

  • Making job titles stand out in your document
  • Adding subheaders with pertinent information
  • Using bulleted lists
  • Leaving plenty of white space
3. Provide Relevant Work Experience

Jobvite study shows that 92 percent of recruiters consider job experience a top hiring factor, but at 83 percent, “culture fit” also garners a top spot in recruiters' considerations.The key is to explore the hiring company’s mission, values, and other digital documents, and then adjust your language, so it aligns with their business.

Now, you can’t make up work experience. However, if you’re applying for financial services data science, highlight experience relating to financials, even if your job title wasn’t data scientist at the time.

4. Add Certifications, Continuing Ed Courses, and Projects

Ongoing learning is a must in the data science field. The fast pace of technology requires applicants to update their skill stack and gain in-demand proficiencies continually.

Of course, if you have a Bachelor’s degree in data science, then place this information under an education section. Any noteworthy certificates, data science courses, and projects you've completed should be placed in a separate segment. Examples may include things like:

  • Hackathons for machine learning and data science
  • Kaggle Competitions
  • Data science courses
  • Publications or projects on your blog or GitHub

Hiring managers are looking for specific skills, like Python and machine learning, but they also want you to demonstrate your ability to solve problems using data. Your publications, case studies, and data science courses offer a more complete picture of your skills.

5. Highlight Your Skills and Data Science Proficiency

Competition for jobs in data science is stiff. The Wall Street Journal reports that the supply of data-savvy analytics workers will double this year.4 This influx of workers, combined with current economic issues, means hiring managers will receive hundreds of applications for a single position.

You can stay ahead of the curve by gaining the skills you need to remain competitive and investing in data science courses that add concrete proof of your abilities to your resume. A recruiter typically will ask in-depth questions about every ability you list on your data science resume.

If you’re not fluent in the subject, leave it off or take the next steps to acquire proficiency. Give the recruiter evidence that you’re equipped for the position by listing skills like:

  • Machine learning, artificial intelligence, and model building
  • Data science languages for statistics and machine learning such as R, SQL, and Python
  • Statistics, business intelligence, and data collection

Gain the confidence to ace interviews by updating your skill set. You can even apply to data science jobs while finishing coursework. Doing so shows the hiring manager that you’re motivated to become a leader in your field.

6. Review Your Resume’s Data Science Keywords

Your data scientist resume must be readable and appealing to humans. But to get it to the recruiter, you need to pass the ATS system. You can achieve the right mix of keywords and context by mimicking the language found in the original job description.

Often, your skills are the keywords, like machine learning or Python. Personalize your data scientist resume by including other terms applicable to the employer such as fraud detection, operations research, R engineering, or anomaly detection.

7. Check Your Resume for Errors

A typo or misplaced comma may seem minor, but your data science resume is one of many. Avoid immediate elimination from the job pool by ensuring an error-free document. Use a tool like Grammarly or ProWriting Aid to assess your grammar and spelling, then pay a friend or an editing professional to give it a once-over.

The second set of eyes helps you identify instances of passive voice and cases where you should add action verbs. An editor may also give tips for visual appearances, such as adjusting your font or sections and title headers.

Stand Out With a Professional Data Scientist Resume

The data science field is booming and highly competitive, so you'll need cutting-edge skills with the latest technology if you want your resume to stand out. Plus, you must exude confidence and competence for every listed ability.

As more students enter the field, successfully portraying your knowledge and capabilities is essential. The best way to secure your future is by looking at requirements for your most desired positions. Then, review your resume and look for gaps.

Expand your current skill set by investing in the best data science courses online.



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