Guide: Resume Optimization for Lever ATS

To optimize effectively, start by understanding how Lever ATS parses and ranks resumes. Lever uses a combination of pattern recognition, keyword matching, and structured data extraction to populate candidate profiles for recruiters. It recognizes headings like Experience, Education, and Skills; it extracts dates and job titles; and it flags keywords that match the job description. But Lever isn’t perfect — inconsistent formatting or unusual fonts can break parsing and drop critical info.

Why this matters: a parsed field that’s empty (like missing dates) can reduce your visibility or trigger manual discard. Here’s how to use that knowledge:

• Use standard section headings (Experience, Education, Skills, Certifications).
• Put dates next to job titles or company names in simple formats (MM/YYYY or YYYY).
• Avoid multi-column layouts, headers/footers with important content, or graphics that contain text.

Quick test you can run: upload your resume to Lever’s candidate portal (or a parser tester online). Check whether key fields — job title, dates, company name, skills — extract correctly. If not, adjust format and retest. Understanding parsing behavior gives you targeted fixes instead of guesswork.

Formatting is the most common reason resumes fail ATS extraction. Follow these proven, practical rules to ensure Lever reads every important field.

Core formatting checklist

• Use a standard font: Arial, Calibri, or Times New Roman, 10–12 pt.
• One-column layout only. Avoid tables and text boxes for primary info.
• Keep margins between 0.5–1 inch to preserve whitespace.
• Save as a clean PDF (unless job posting requests Word). Avoid scanned images.
• Use plain bullet points (• or -); avoid special symbols.

Date and title placement

Place dates to the right of the job title or directly under the company name on a single line. Examples:

• Product Manager | Acme Corp | 06/2019 — 08/2023
• Acme Corp — Product Manager (Jun 2019–Aug 2023)

Headings and hierarchy

Use consistent H2-style headings for sections. Lever looks for common labels so don’t invent creative section titles. Replace "What I’ve Done" with "Experience"; use "Education" not "Schooling." These small changes significantly improve parsing success.

Lever’s keyword matching favors resumes that mirror the job description’s language. But keyword stuffing looks unnatural and fails human reviewers. Here’s a strategic approach:

Steps to build a targeted keyword map

1. 1Extract 8–12 primary keywords from the job posting (skills, tools, certifications, role-specific phrases).
2. 2Group into categories: Technical Skills, Tools/Platforms, Methods, Outcomes.
3. 3Map at least one keyword into each relevant bullet point under Experience — not a single list.

Example: For a "Growth Marketing Manager" role, primary keywords might be: "paid acquisition," "A/B testing," "Meta Ads," "SQL," "LTV," and "funnel optimization." Instead of listing them in a Skills box, write impact bullets such as:

• Increased paid acquisition ROAS 35% by A/B testing ad creative and refining Meta Ads bidding strategies.
• Built SQL-based dashboards to track funnel conversion rates and LTV by cohort.

Long-tail and semantic phrases

Lever rewards context. Use long-tail phrases that match how hiring teams write job postings: "B2B SaaS paid acquisition" vs. just "paid acquisition." Use verbs and measurable outcomes to connect skills to results.

Recruiters want measurable results; Lever looks for keywords. Combine both by writing bullets that are concise, quantified, and jargon-light.

Bullet structure to use (proven template)

• Action verb + what you did + tool/skill + measurable result + context.

Example before/after:

Case study — Senior Data Analyst (realistic scenario)

• Original: "Worked on analytics and dashboards."
• Optimized: "Designed SQL-driven dashboards and automated reporting that reduced monthly reporting time from 12 hours to 2 hours and improved data-driven product decisions, increasing feature adoption by 18%."

Actionable tips

• Include quantifiable outcomes (%, $ amounts, time saved).
• Mention tools (e.g., Salesforce, Lever, Looker) where relevant — but in context.
• Keep bullets to one or two lines; avoid run-on sentences.

These changes help Lever detect both skills and measurable impact, improving your match score and persuading the recruiter.

Listing skills effectively tells Lever which buckets to place you in. But placement and specificity matter.

Two-place rule

Place your most relevant skills in two places: a concise Skills section and woven into role-based bullets. This redundancy increases visibility without keyword stuffing.

Skills section best practices

• Keep it to 8–12 core skills for the role.
• Use commas, not vertical bars. Example: "Product Management, Roadmapping, A/B Testing, SQL, Mixpanel, GTM Strategy."
• Order by relevance, not alphabetically.

Certifications and licenses

List certifications with issuing organization and date (or year). Example:

• Google Analytics Individual Qualification — Google, 2023
• PMP — PMI, 2021

Example placement

Skills section:

• Technical: SQL, Python, Looker
• Marketing: Paid acquisition, Meta Ads, Email automation

Experience bullet:

• "Built automated Looker reports using SQL and Python, reducing report delivery time by 80%."

This structure helps Lever map your abilities to job requirements and gives recruiters quick topical scanning hits.

Optimization is not one-and-done. Use testing and measurement to iterate toward higher match rates.

Quick tests you can run

• Parser test: Upload resume to Lever or use a public resume parser to confirm fields extract correctly.
• Job-match test: Use job description keyword density tools or simple text comparison to see overlap.
• A/B test: Create two versions (one keyword-rich, one narrative-heavy) and submit them across similar roles to compare response rates.

Metrics to track

• Replies or interview invites per application (%).
• Phone screens per X applications.
• Time from application to first reply.

Real-world iteration example

Case: A product manager applied to 50 roles with low response. After adjusting format, adding targeted keywords, and emphasizing measurable outcomes, replies rose from 4% to 18% over the next 40 applications. The candidate documented which keywords correlated with replies and refined language for subsequent applications.

Tools and automation

• Use resume parsers and keyword matchers (many are free) to score resumes against job descriptions.
• Consider using a tool like Resumize.ai to automatically analyze and recommend optimizations for specific Lever job postings. Resumize.ai can speed up iteration and surface the highest-impact changes, especially when you’re applying to multiple Lever-powered roles.

Avoiding common pitfalls saves time and prevents lost opportunities. Here’s a focused list of mistakes and a final checklist to run before you submit to Lever.

Common mistakes

• Fancy templates with columns and graphics that break parsers.
• Using nonstandard section titles or omitting dates.
• Keyword stuffing without context.
• Not tailoring experience to the role’s highest priorities.

Final submission checklist (do this in 10 minutes)

1. 1Convert your resume to PDF and re-open to ensure no formatting shifts.
2. 2Run a parser test and confirm Experience, Education, Skills, Dates extract correctly.
3. 3Check that your top 8–12 role-specific keywords appear at least twice across resume sections.
4. 4Make sure each role has 3–5 achievement bullets with quantifiable outcomes.
5. 5Keep file name simple: FirstLast_Role_Resume.pdf

What if Lever still rejects important fields?

If parsing still fails, send a plain-text resume or Word doc when the job application allows, or include key details in the online application fields. Also, follow up via LinkedIn or email to add a human touch.