ATS Resume Sample: A Full Example, Side-by-Side With Its Non-ATS Twin

One candidate, two resumes, two outcomes

Jordan Martinez has the same qualifications no matter which version of her resume she submits. She still led a demand generation team of six. She still drove $3.2M in pipeline contribution. She still raised MQL-to-SQL conversion by 47%. None of that changes between Version A and Version B.

What changes is the format. Version A is single-column, plain Calibri 11pt body, contact info in the document body, standard section headers, comma-separated skills list, exported as .docx. Version B is two-column with a sidebar, custom display font for the name, skill-bar graphics, a header banner with a photo, exported as a Canva-designed PDF.

That delta is not a quirk of one parser. It repeats across every ATS we tested. And it is the difference between an interview and a silent rejection, because 97.8% of Fortune 500 companies use applicant tracking systems to screen resumes before any human reads them (Jobscan Fortune 500 Report, 2025). With Workday alone handling roughly 39% of Fortune 500 hiring, the parse-rate of Jordan's resume directly controls whether her name reaches a recruiter at all.

Meet Jordan Martinez

Jordan is a fictional but realistic mid-career candidate built to expose how formatting choices interact with parser behavior. Her credentials:

• 8 years of B2B marketing experience, currently Senior Marketing Manager at a 200-person SaaS company
• Leads a demand generation team of 6 people
• Owns $3.2M in annual pipeline contribution
• Raised MQL-to-SQL conversion from 9% to 13.2% (a 47% relative improvement) over 18 months
• Stack: HubSpot Marketing Hub, Marketo, Salesforce, 6sense (ABM), Google Analytics 4, Looker
• Education: BA, Marketing, University of Texas at Austin, 2016
• Targeting: Senior Marketing Manager role at a Series-C B2B SaaS competitor

On paper, she is a strong candidate. The hiring manager would interview her. The recruiter would shortlist her. The question is whether the parser ever shows the recruiter her name in the first place.

The job description she is targeting

The target role is "Senior Marketing Manager, Demand Generation" at a Series-C B2B SaaS competitor. The job description names twelve critical keywords that the ATS will look for when scoring candidates:

Both versions of Jordan's resume include all twelve concepts. The difference is whether the parser can extract them. As Jobscan's research shows, 76.4% of recruiters filter by skills and 55.3% filter by previous job titles (Jobscan, 2025). If a parser cannot read the skills section, those filters silently exclude the candidate.

The side-by-side: Version A versus Version B

Below is Jordan's resume rendered in both formats. The left column is the ATS-optimized .docx. The right column is the designer PDF. Read both. Notice that the qualifications are identical. The only thing that changes is the structure.

Visually, Version B looks more impressive. It feels modern. It would print beautifully. But the parser does not see it the way a human eye does. The parser sees a stylized header banner where contact information lives, a sidebar where skills sit as graphics, custom fonts that may rasterize as images during PDF export, and date strings written in three different formats. Each of those choices costs structural points. We measured how many.

What works in Version A: annotated

Each formatting choice in Version A maps to a measurable parsing or scoring outcome. Here is what Jordan got right and why each decision matters.

What breaks in Version B: annotated

Version B looks like effort. It looks like care. The candidate clearly spent time in Canva. None of that effort survives the parser. Here is what fails and why.

The parser pass-rate matrix

We ran both versions of Jordan's resume through five applicant tracking systems: Workday, Greenhouse, Lever, iCIMS, and Taleo. "Pass rate" measures how much of the resume the parser successfully extracted into structured fields: contact information, work history with dates and titles, skills, and education. A 100% pass rate means every field came through cleanly.

The match score row matters most. Even when Version B's parser pass rate looks tolerable on Greenhouse at 52%, the cascading effect on keyword detection drops the final match score to 41%. Once a parser misses your skills section, every downstream filter reads you as unqualified, regardless of what your work history actually says.

Methodology note: Pass-rate figures are based on Resume Optimizer Pro's internal parser-testing framework using publicly documented behavior of each ATS combined with controlled side-by-side extraction tests. We do not have access to ATS internals. Figures should be read as relative deltas, not absolute SLAs.

Row-by-row diff: what changed between the two

Eight format choices separate Version A from Version B. Each one carries an independent cost. Here they are side by side.

No single row in this table is a fatal mistake on its own. A two-column resume can sometimes survive Greenhouse. A header banner can sometimes survive iCIMS. But the costs stack. Eight independent format choices, each shaving 2 to 12 points off the match score, compound into the 87% versus 41% delta. For a deeper look at how parsers actually read and tokenize a resume, see our companion piece on how resume parsers actually work.

A single bullet, before and after

Format is half the story. The bullets themselves also matter. Here is one of Jordan's bullets in two forms: the cluttered version she might have written first, and the clean version that ended up in Version A.

The "after" bullet does four things at once: it leads with a strong verb (Raised), it quantifies the outcome (9% to 13.2%), it names the tools the JD asked for (HubSpot, Salesforce), and it closes with a dollar impact ($3.2M). All four are visible to a parser. The "before" bullet is invisible to keyword filters and forgettable to humans. Same job, two completely different signals.

Get this template

We do not host the .docx as a static file. Instead, the matcher tool generates an equivalent ATS-optimized resume customized to your job description. Upload your current resume, paste the JD you are targeting, and see the same parser pass-rate breakdown applied to your own document. That is more useful than downloading Jordan's resume and replacing her details, because your match score depends on how your specific experience maps to your specific JD.

If you do want to mirror Jordan's structure manually, the rules are simple: single column, contact in body, "Summary / Experience / Skills / Education" headers, Calibri 11pt body, comma-separated skills, "Mon YYYY - Mon YYYY" dates, .docx export. That structure alone covers the parsing part. The keyword and quantification work is what gets you from 70% to 90% on your match score.

The "but Version B looks better" argument

Most candidates know on some level that Version B is risky. They submit it anyway, because Version A looks plain. This section addresses that tension head-on.

Yes, the designer version looks more impressive in a portfolio. Yes, recruiters will tell you they appreciate clean, modern design. Both can be true and still lose you the interview, because the recruiter never sees the resume unless the parser passes it through first. The 11.2-second average recruiter scan time (InterviewPal, August 2025) is a real number, but it is the second filter, not the first. The first filter is the parser, and the parser does not care about visual design.

The compromise that actually works:

• For ATS-routed applications (anywhere you upload a resume to a job board, company portal, Workday/Greenhouse/Lever/iCIMS/Taleo, LinkedIn Easy Apply): use Version A.
• For direct-to-human applications (career fairs, recruiter outreach, networking introductions, hand-delivered resumes): a designed version is fine. Save it as a separate file. Send it after the human has expressed interest.

The mistake is choosing one resume to rule them all. The right answer is two resumes: an ATS-optimized .docx for systems and a portfolio-style version for humans. Maintain both. Use the right one for the right channel. For more on how to structure the ATS version specifically, see our guide on what an ATS resume actually looks like.

How to adapt this sample for your role

Jordan is a marketing manager. The structure of Version A is role-agnostic. Here is the five-step adaptation path.

1. Replace contact details. Swap Jordan's name, city, email, phone, and LinkedIn URL with yours. Keep the same plain-text format on the first two lines.
2. Rewrite the summary. Use the same three-element formula: years of experience plus role focus, two or three function areas owned, one or two quantified outcomes. Keep it to three lines.
3. Replace experience bullets with your own achievements. Use the strong-verb plus quantified-outcome formula from the bullet rewrite section above. Three to five bullets per role. Lead with the highest-impact result.
4. Update the skills list. Match it to your stack. Include both the term as written in the JD and any common synonym. The skills section is comma-separated text, not a table.
5. Run it through a matcher. Verify a 70%+ match against the specific JD you are targeting. If the score is below 70%, work backward from the missing keywords. Tailored resumes generated 11.7% callback rates versus 4.2% for generic in a 15,000-application study (2024).

For a deeper dive into the score number itself and how the matcher calculates it, see our ATS resume score guide.

Common ATS resume sample mistakes

We see the same handful of formatting mistakes in nearly every resume submitted to the matcher. Avoid these and you will already be in the top quartile of submissions.

88% of employers reject qualified candidates because of resume formatting (Harvard Business School / Burning Glass, 2024). The "qualified" part matters. These rejections are not about candidate ability. They are about the resume being unreadable to the parser. Format errors are the cheapest fix in the entire job-search funnel.

The bottom line

Jordan Martinez's qualifications never changed. Her experience at NorthStar SaaS, Lumenfield, and BrightPath stayed the same. Her $3.2M pipeline contribution, her 47% MQL-to-SQL lift, her HubSpot and Marketo and 6sense expertise, all unchanged. What changed was format. And format took her from 87% match to 41% match, from clean parse to garbled parse, from interview-eligible to silently rejected.

The difference between Version A and Version B is not talent or effort. It is awareness of how applicant tracking systems read resumes. The candidates who win the parser stage are not better candidates. They are better-formatted candidates. That is a fixable gap.