Crafting a Data Resume That Makes Recruiters Swipe Right (Data Coach Insights)

In today's competitive data job market, your resume serves as the critical first impression that can make or break your job search. With thousands of applicants vying for the same roles, how do you ensure your resume not only makes it past the initial screening but actually catches a recruiter's eye?

To help aspiring data professionals stand out in the crowd, Kishawna Peck, CEO of Womxn in Data Science, recently shared her expert insights during our Data Career Masterclass webinar. With 10 years of experience leading data teams across various industries and founding Canada's first conference for women in data, Kishawna has a deep understanding of what hiring managers look for in data resumes.

This guide distills her proven strategies to help you create a data resume that stands out to recruiters.

Understanding the Job Application Process

Before diving into resume optimization, it's important to understand the full job application journey. There are multiple paths to landing a data role:

• Direct search (company job boards, LinkedIn, job sites)
• Recruiters (direct outreach or talent agencies)
• Network referrals (leveraging your professional connections)

But Kishawna also shared a "cheat code" that many candidates overlook: analyzing your LinkedIn network to discover hidden opportunities.

"A cheat code that I have for finding different roles is actually to analyze your LinkedIn network. You can download your LinkedIn connections... You might see different companies that you never would have thought of applying to."

The LinkedIn Network Analysis Technique

Here's how you can leverage this powerful but underutilized approach to expand your job search horizons:

1. Download your LinkedIn connections data (Settings & Privacy → Data Privacy → Get a copy of your data)
2. Analyze the data to identify companies where your connections work
3. Look for non-traditional industries that might be building data teams
4. Create a targeted list of companies to research for open roles

Non-traditional industries often have less competition for data roles and may be more open to entry-level applicants. As Kishawna points out, "They're starting their data teams and [are] more open to entry-level applicants as well."

Job Description Analysis: Breaking Down the Requirements

One of the most common application challenges identified by webinar participants was meeting job requirements. Kishawna offers a refreshing perspective on how to read job postings:

"The first two to three requirements, those are the must-haves. Anything after that, I kind of see as the wish list of the employer."

Job seekers often rule themselves out prematurely by believing they need to meet 100% of the requirements. Kishawna recommends aiming to match about 60-70% of the listed skills and requirements instead.

Another important insight: don't let years of experience requirements intimidate you. This is particularly true for entry-level positions. As Kishawna notes, "I wouldn't let the years of experience be something that would make me not apply."

Decoding Hidden Requirements

Job descriptions often contain coded language that hints at workplace culture and unstated expectations. Understanding these can help you tailor your resume more effectively:

Resume Structure & Formatting Best Practices

Now that you understand how to decode job descriptions, the next step is building a resume that effectively addresses these requirements. Let's examine the key components every data resume should include to make the strongest possible impression:

1. Name and contact information
   • Include LinkedIn, email, GitHub, and portfolio links
   • Make links clickable for easy access
2. Technical skills
   • Group by category (languages, tools, platforms)
   • Be specific about libraries and frameworks
3. Professional experience
   • List in reverse chronological order (most recent first)
   • Quantify results and impact
   • Use strong action verbs (developed, built, created)
4. Projects
   • Essential for entry-level positions
   • Include tools used and outcomes achieved
5. Education
   • Usually placed at the bottom
   • Include relevant certifications

Formatting is just as important as content. Kishawna strongly recommends:

"A single column layout makes the most sense... it's actually easier for ATS systems to read single column resumes than multi-column ones."

Other key formatting tips include:

• Clear section headers
• Consistent spacing (white space helps the reviewer scan your resume)
• Standard formatting without creative but confusing elements

Things to know about certifications:

• Python
• Data analytics
• Power BI
• SQL

Common Resume Mistakes to Avoid

• Using star/bar ratings for skills (not ATS-friendly)
• Vague project descriptions lacking technical details
• Missing metrics and quantifiable results
• Not including links to your projects

Understanding Your Application Audience

Your resume faces two distinct audiences with different needs:

1. Applicant Tracking Systems (ATS)
   • 97% of Fortune 500 companies use them
   • 75% of job applications fail at this stage
   • Looking for specific keywords and standard formatting
2. Human Reviewers
   • Spend less than 10 seconds on initial screening
   • Read in F-shape and Z-shape scanning patterns
   • Need scannable, well-organized information

Understanding this dual audience explains why certain resume formats work better than others. As Kishawna explains:

"The human reviewer needs it to be scannable. These motions are based on how humans read... This allows them to spend those 10 seconds in a valuable way."

Optimizing for ATS Systems

ATS systems are the first gatekeepers your resume encounters. To maximize your chances of getting past them, focus on:

What ATS Systems Look For

• Required technical skills using exact terminology
  • Be explicit about technologies (e.g., say "Python" instead of "programming languages")
  • List specific libraries and tools mentioned in the job description
• Job titles and chronological experience
  • Standard job titles are better recognized
  • Clear timeline of your experience
• Standard formatting
  • Single column layouts
  • Standard section headings (e.g., "Work Experience" instead of "My Journey")

"Don't get creative with the headings. Have something like 'work experience' instead of 'my journey.'"

ATS Optimization Example

Less Effective:

SKILLS
Programming: ★★★★☆
Data Visualization: ★★★★★
Machine Learning: ★★★☆☆

More Effective:

TECHNICAL SKILLS
Languages: Python, SQL, R
Data Visualization: Tableau, Power BI, Matplotlib, Seaborn
Machine Learning: Scikit-learn, TensorFlow, regression, classification, clustering

Tools for Resume Optimization

Kishawna suggests using AI tools to help analyze and optimize your resume, with careful review of the results:

"There is a prompt to help you optimize your resume for both the ATS and human reviewers... But remember to review the outputs because as you know, with large language models... sometimes it might embellish some of your experience."

Important considerations when using AI tools:

• Remove personal information before sharing your resume with any AI tool
• Always review AI suggestions critically
• Ensure your resume still accurately reflects your experience
• Verify you can defend everything on your resume during an interview

Next Steps and Resources

During the webinar, Kishawna conducted a poll asking participants about their top resume priorities. The results revealed several key areas where job seekers identified they needed the most improvement:

• Rebuilding resume format (37%)
• Improving quantifiable results (28%)
• Optimizing keywords (20%)
• Enhancing project descriptions (15%)

These results highlight common challenges many data professionals face. By addressing these same areas in your own resume, you'll be tackling the issues that most frequently prevent qualified candidates from advancing in the application process.

To support your resume development journey, Dataquest offers several resources:

• Data Science Resume Optimization Guide includes prompts for analysis, ATS optimization, and self-assessment.
• Data Analytics Projects that can enhance your portfolio
• The Dataquest Community where you can share projects and get feedback

Final Thoughts

Getting your resume right is just one step in the job search process, but it's a critical one. As Kishawna reminds us:

"Knowing that the chances are so slim in that 10 seconds... even if you don't get a role but you've been screened and went through some interviews, you should really pat yourself on the back because this is a process. You're getting closer and closer to that next role."

Your resume is your ticket to the interview, where you can really shine. By implementing these expert tips, you'll create a resume that effectively showcases your skills, passes ATS screening, and catches recruiters' attention—making them want to swipe right on your application.