The Resume We Started With (And Why It Was Underscoring)
If you've ever wanted to see what a resume before and after optimization actually looks like — not a vague "we improved it" claim, but real line-by-line changes and a measurable score movement — this walkthrough is for you. We ran a complete optimization pass on a sample resume, documented every change, and re-scored it with the same engine. Here's exactly what happened.
Meet the Sample Candidate
Marcus is a mid-level project manager with six years of experience, transitioning from a traditional enterprise environment to a SaaS company. His resume was well-organized, spell-checked, and clearly laid out his career progression. It was not a badly written resume.
His initial ATS score: 41 out of 100.
That number surprised him. It shouldn't have. A low ATS score rarely signals poor writing — it almost always signals a targeting problem. Marcus had described his work in the language of his previous employer, not in the language of the job description he was applying to.
Here's what the six-layer scoring engine flagged on the initial pass:
- Keyword coverage: 9 of the top 22 job-description keywords were absent from the resume entirely
- Impact verb strength: The majority of experience bullets opened with weak or passive constructions ("Responsible for," "Helped with," "Worked on")
- Skills section density: 11 skills listed, unstructured, with no alignment to the role's requirement clusters
- Summary relevance: The opening paragraph described Marcus's background generically, with no signal toward the specific role or company type
The other four layers — formatting, education match, years-of-experience signals, and contact/section completeness — scored adequately. Those weren't the problem.
Reading the Keyword Gap Report
The gap report is the first real lever for improving a resume's ATS score. It tells you not just what's missing, but how much each gap costs you.
High-Weight vs. Lower-Weight Keywords
When Marcus's resume was run against the job description — a Senior Project Manager role at a growth-stage SaaS company — the missing keyword list broke down like this:
High-weight keywords (appearing in the job title, core requirements, or repeated multiple times):
- cross-functional alignment
- OKRs (Objectives and Key Results)
- stakeholder reporting
- Agile delivery
- roadmap planning
Lower-weight keywords (mentioned once, in nice-to-have or preferred sections):
- JIRA
- sprint retrospectives
- capacity planning
- executive updates
High-weight keywords carry more scoring impact because ATS systems — and the humans reading parsed output — treat frequency and placement in the original job description as a proxy for importance. If the role requires "Agile delivery" and your resume never uses that phrase, even if you've run sprints for four years, the system sees a gap.
That's the frustrating reality: Marcus had done this work. He'd facilitated cross-functional teams, tracked progress against quarterly goals, and presented weekly status reports to VP-level stakeholders. But none of that experience was described in the vocabulary the job description used. The work was there. The signal wasn't.
The Optimization Pass: What Changed Line by Line
This is the resume rewrite example portion most people skip to — and it's worth reading carefully, because the changes are more surgical than most people expect.
One rule applied throughout: every term introduced in the rewrite was already present in Marcus's actual experience. The rewrite rephrased and repositioned. It did not invent responsibilities, inflate titles, or insert skills he didn't have.
Before & After: Three Concrete Rewrites
Example 1 — Professional Summary
Before: "Results-driven project manager with 6+ years of experience leading teams and delivering projects on time and under budget. Strong communicator with a track record of cross-team collaboration."
After: "Senior Project Manager with 6 years driving cross-functional alignment across engineering, product, and commercial teams in fast-paced environments. Experienced in Agile delivery, OKR-based roadmap planning, and stakeholder reporting to C-suite and VP-level audiences."
What changed: The vague opener ("results-driven," "strong communicator") was replaced with specific positioning. Three high-weight keywords — cross-functional alignment, Agile delivery, OKR-based roadmap planning — were surfaced from Marcus's actual history and placed in the highest-visibility section of the resume. Nothing was invented.
Example 2 — Experience Bullet
Before: "Managed project timelines and coordinated with multiple teams to deliver work on schedule."
After: "Led cross-functional delivery of 6 concurrent workstreams against OKRs, reducing average cycle time by 18% over two quarters through structured sprint retrospectives and capacity planning."
What changed: The original bullet described activity (managing, coordinating). The rewrite describes outcome and scale. The specific figures — 6 workstreams, 18% cycle time reduction — came directly from Marcus's own notes during intake. Real numbers, now visible. The keywords OKRs, sprint retrospectives, and capacity planning were drawn from his background, not inserted from nowhere.
Example 3 — Skills Section
Before: Skills: Microsoft Project, PowerPoint, Stakeholder Management, Budgeting, Risk Management, Agile, Scrum, Excel, Communication, Leadership, Team Building
After:
| Category | Skills |
|---|---|
| Delivery Methodologies | Agile Delivery, Scrum, Sprint Planning, Retrospectives |
| Stakeholder & Reporting | Stakeholder Reporting, Executive Updates, Cross-functional Alignment |
| Planning & Tools | OKR Tracking, Roadmap Planning, Capacity Planning, JIRA, Microsoft Project |
| Core Competencies | Risk Management, Budget Oversight, Team Leadership |
What changed: An unstructured list became a categorized, keyword-aligned block. Every skill was already on Marcus's resume or mentioned in his work history — reorganized to match the structure that ATS parsers and recruiters both prefer.
The Score After: What Moved and What Didn't
Post-optimization score: 74 out of 100.
That's a 33-point improvement. Here's where the movement came from:
| Scoring Layer | Before | After |
|---|---|---|
| Keyword Coverage | 28/40 | 38/40 |
| Impact Verb Strength | 6/15 | 13/15 |
| Skills Section Density | 4/10 | 9/10 |
| Summary Relevance | 3/10 | 8/10 |
| Formatting & Parseability | 8/10 | 8/10 |
| Education Match | 5/5 | 5/5 |
| Years of Experience | 5/5 | 5/5 |
| Contact & Section Completeness | 5/5 | 5/5 |
| Total | 41/100 | 74/100 |
What Didn't Move (And Why That's Fine)
Four layers held steady: formatting, education, years of experience, and section completeness. These were already passing — and two of them (education match, years of experience) aren't rewritable anyway. If the job requires a PMP certification and Marcus doesn't have one, no amount of keyword work changes that signal.
This is what deterministic scoring makes possible: the re-score used the exact same six-layer engine as the initial scan. The gain is verifiable because the method is consistent. There's no model estimating whether the resume "feels better" — it's the same calculation, run twice, with different inputs.
One honest note: a score of 74 improves the likelihood that Marcus's resume clears automated filters and reaches a recruiter's review queue. It does not guarantee an interview. Fit, competition, timing, and the human reviewer all still matter.
What Resume Optimization Cannot Fix
This section exists because accuracy matters more than hype.
Hard Mismatches
If Marcus were applying for a VP of Engineering role, no resume optimization would help him clear a threshold that requires 15 years of experience when he has 6. Optimization surfaces what's there — it can't create qualifications that aren't.
Similarly, if a role lists a required certification (PMP, CISSP, CPA) and Marcus doesn't hold it, that gap persists regardless of keyword coverage.
Structural Formatting Problems
Some resumes are unparseable before keyword work even begins: tables that swallow bullet text, graphics where a skills section should be, headers embedded in text boxes that get stripped entirely. Keyword optimization on a malformed document is like renovating a house with foundation damage. Fix the structure first.
Targeting the Wrong Role
The highest-scoring resume is still a targeting failure if it's aimed at the wrong job. A resume optimized for a Senior PM role at a SaaS company is not automatically well-optimized for a construction project superintendent or a program manager in federal contracting. Optimization is only as effective as the role-fit behind it.
How to Run This Process on Your Own Resume
Here's the sequence, whether you're doing this manually or using a tool like ATSEye:
Step 1: Establish a baseline Upload your resume and paste the target job description. Don't edit anything yet — you need a clear starting point before you change anything.
Step 2: Prioritize the gap report Focus on high-weight keywords first: those appearing in the job title or listed requirements. For each one, ask honestly — do I have genuine experience with this? If yes, it belongs in your resume. If no, don't add it.
Step 3: Rewrite with the gaps in mind Surface keywords you've earned into your summary, experience bullets, and skills section. Lead bullets with action verbs and outcomes, not activity descriptions. If you use an AI rewrite, read every line before accepting it. Confirm that every term added reflects something you've actually done.
Step 4: Re-score before you send Run the optimized version through the same scoring engine. Confirm the improvement is real. If a layer barely moved, investigate why before submitting.
Step 5: Tailor per role One optimized resume sent to 40 different jobs is still a targeting problem. A product manager role at a fintech company and a project manager role at a healthcare SaaS company will share some keywords and diverge on others. Tailoring takes 10–15 minutes per application once you have a solid base version to work from.
What to do today: Pull up the job description for the next role you're seriously interested in. Read the requirements section and highlight every phrase that describes work you've genuinely done. Then open your resume and check how many of those phrases appear — verbatim or close to it. That gap list is your optimization roadmap, and it costs nothing to build.
Written by
ATSEye Editorial Team
ATS & resume-scoring specialists
The ATSEye editorial team writes about applicant tracking systems, resume optimization, and hiring — grounded in how our deterministic, six-layer scoring engine actually parses and scores resumes.