Free ATS Keywords by Industry
Discover the most important resume keywords for your industry. Beat the ATS and land more interviews in 2025.
of resumes are rejected by ATS due to missing keywords
industries covered with specialized keyword lists
carefully curated ATS keywords
How the Keyword Library Is Built and Kept Current
The 500+ keywords in this tool are not a static list assembled once and left alone. They come from three sources, applied on a rolling basis: analysis of the job descriptions processed by TalentTuner across 50,000+ resume analyses, patterns in the job listings indexed by major job boards (LinkedIn, Indeed, ZipRecruiter), and terminology from professional standards bodies and certification programs in each covered industry.
How keyword sourcing and update cadence works for each industry
| Industry | Primary keyword sources | Key nomenclature bodies | Typical vocabulary drift rate |
|---|---|---|---|
| Technology & Software | LinkedIn Tech jobs, Stack Overflow Jobs archive, GitHub job board | IEEE, CNCF, AWS/Azure/GCP certification catalogs | High — new frameworks and platforms emerge quarterly |
| Healthcare & Medical | Indeed Healthcare, Health eCareers, AAMC job listings | AHIMA, AANP, ANCC certification catalogs; ICD-10/CPT codes | Medium — regulatory and certification vocabulary stable; EHR platform vocabulary shifts with market consolidation |
| Finance & Banking | eFinancialCareers, LinkedIn Finance, Bloomberg job board | CFA Institute, FINRA, ACCA, CPA license terminology | Low–Medium — core vocabulary stable; fintech and regulatory keywords shift more rapidly |
| Marketing & Digital | LinkedIn Marketing jobs, MarketingHire, Indeed | Google Analytics Academy, HubSpot Academy, IAB terminology | High — platform names (GA4, Meta Ads Manager) and privacy regulation terminology (GDPR, CCPA) change often |
| Project Management | LinkedIn PM jobs, PMI job board, Glassdoor | PMI (PMP certification), Scrum Alliance, PRINCE2, SAFe framework | Low–Medium — methodology names (Agile, Scrum, Kanban, SAFe) stable; tool names (Jira, Asana, Monday.com) shift with adoption |
The full scoring model that TalentTuner applies to your specific job description — including how TF-IDF keyword weighting differs from static lists — is documented on the methodology page. The difference matters: this keyword browser shows industry-level vocabulary; TalentTuner's analysis weights terms by their actual frequency in the specific job description you submit.
Critical Keywords Versus Preferred Keywords — Why the Distinction Changes Your Strategy
Not all keywords in a job description carry equal weight. TalentTuner's TF-IDF matching distinguishes between terms that appear repeatedly in the job description (high-frequency, high-weight — effectively required) and terms that appear once in passing (low-weight — preferred or contextual). The keyword browser below shows industry-level vocabulary, but within any given job description, the same terms will be distributed across this spectrum.
Critical vs. preferred keyword extraction — how TalentTuner weights them and how you should prioritise them
| Keyword class | How it appears in a JD | TF-IDF weight | Your prioritisation |
|---|---|---|---|
| Critical / required | Appears 3+ times; in "Required Qualifications" section; in the role title itself | High | Must appear in your resume; absence will materially lower your match score; include in summary, skills section, and at least one bullet |
| Preferred / mentioned | Appears once or twice; in "Nice to Have" or "Preferred" section | Medium | Include if genuinely relevant; a single mention in your skills section is often sufficient; do not force-fit |
| Contextual / background | Industry-standard terms that appear as context, not requirements ("our team uses Jira" without specifying Jira as a requirement) | Low | Include if you have genuine experience; adds signal but missing them will not significantly hurt your match score |
| Boilerplate / generic | Words like "collaborative," "detail-oriented," "fast-paced environment" — appear in nearly every JD | Near-zero (IDF is very low for terms that appear in nearly all documents) | These do not move your TalentTuner score meaningfully; spend time on critical and preferred terms instead |
The TF-IDF weighting logic is explained in more detail on the methodology page and in the ATS match model section of the whitepaper. The keyword browser on this page gives you the vocabulary of the industry; running the full analysis against your specific job description assigns the weights.
Keyword Density — What the Ranges Actually Mean
Keyword density figures circulate widely but are rarely grounded in how ATS platforms actually score. The table below reflects the observed patterns from TalentTuner's analysis corpus and how they map to ATS scoring behavior across Workday, Oracle Taleo, Greenhouse, and Lever.
| Keyword density range | What it typically indicates | ATS scoring effect | Industry variation |
|---|---|---|---|
| Below 1% for critical terms | Critical keywords appear only once or are missing; resume reads as generic | Low match score; Taleo requisition rank will be low; Workday screening questions unrelated to keyword match but recruiter search will not surface this candidate | Tech roles particularly sensitive; a single mention of "Kubernetes" versus three mentions in context (summary + skills + bullet) is a meaningful difference |
| 1–3% for critical terms | Keywords appear in context — summary, skills section, at least one bullet — without forced repetition | Strong match signal; typical range for high-scoring resumes in TalentTuner's corpus | Healthcare and finance roles: slightly lower density acceptable because longer job titles absorb keyword space differently |
| Above 4–5% for any single term | Keyword stuffing; unnaturally high repetition | Modern ATS platforms (Greenhouse, Lever) are less susceptible to stuffing than older systems (some Taleo versions); human recruiter review is the stage where stuffing is most reliably detected and penalised | Risk is universal; no industry benefits from stuffing |
| Abbreviation vs. full form parity | Including both "Python" and "Python programming" or "SEO" and "search engine optimization" once each | Improves match against both abbreviated and full-form queries; TalentTuner's spaCy lemmatisation normalises some variants, but not all ATS parsers do | Healthcare and finance especially benefit: "RN" vs. "Registered Nurse," "CPA" vs. "Certified Public Accountant" are queried differently by different recruiters |
Targeting the 1–3% density range for the three to five most critical terms in a given job description is a more reliable strategy than maximising total keyword count. Coverage of critical terms matters more than volume of total terms.
For Specific Situations: How to Use This Tool in Your Context
If you do not have a job description yet and need a starting keyword list:
Select your target industry from the tool above to generate the industry's standard vocabulary. Treat the output as the vocabulary floor — these are the terms that appear consistently across roles in this field. Before you apply to any specific role, run the full TalentTuner analysis with the specific job description, because the gap between industry-level vocabulary and a single role's actual requirements is often significant. A data scientist role at a healthcare company will pull heavily from both the Technology and Healthcare keyword sets in ways that no pre-built industry list can predict. Use this tool to build familiarity with the vocabulary; use the full analysis to target a specific role.
If you are switching industries and do not know which keywords matter in your target field:
Browse the keyword list for your target industry first and note which terms you have encountered in your work, even if you called them something different. Industry vocabulary often describes the same competency with different terminology — "stakeholder management" in Finance becomes "cross-functional collaboration" in Technology, and "case management" in Healthcare maps closely to "project coordination" in general business roles. The comparison table below shows common cross-industry vocabulary equivalencies to help you identify what you already have but may not be surfacing with the right language.
| Your current field uses: | Target field (Tech) uses: | Target field (Finance) uses: | Target field (Healthcare) uses: |
|---|---|---|---|
| Managing a team | Engineering management / team lead | Team leadership / people management | Staff supervision / charge nurse / shift lead |
| Analysing data | Data analysis / SQL / Python / Tableau | Financial modelling / Excel / Bloomberg / BI | Clinical data analysis / Epic / outcomes reporting |
| Managing projects | Agile / Scrum / Jira / product delivery | Project management / PMP / deal execution | Quality improvement / process improvement / care coordination |
| Working with regulations | Compliance / SOC 2 / GDPR / security frameworks | Regulatory compliance / SEC / FINRA / Basel III | HIPAA / CMS / Joint Commission / regulatory reporting |
Once you have identified the relevant vocabulary mapping, update your resume to use the target field's terms, then run the full analysis against a real job description in that field to validate your match score. The methodology page explains how TalentTuner's intent-fit layer evaluates whether your experience signals fluency in the target industry's vocabulary.
If you have a list of skills but they are written in your old industry's vocabulary:
This is one of the most common patterns in career-change resumes and one of the most fixable. The underlying competency is real; the labeling is wrong for the target ATS. The keyword browser above can act as a vocabulary reference: find the target industry, identify the terms that map to your existing skills, and update your skills section and bullet points to use the target vocabulary. ATS systems including Workday, Oracle Taleo, Greenhouse, and Lever all use keyword matching against the candidate's submitted text — they do not interpret intent, infer equivalencies, or translate between industries. If your resume says "census data analysis" and the job description says "SQL-based data analysis," the ATS sees two different terms. The translation is your job, and this tool is designed to help you do it systematically before you submit.
If you want to validate that your skills section covers the field's standard vocabulary:
Browse the keyword list for your industry, then cross-reference it against your current skills section. Any keyword category where you have zero representation is either a genuine gap or a labeling issue — and the keyword browser helps you tell the difference. If the Technology list shows "CI/CD pipeline" and you have never worked with continuous integration tools, that is a genuine gap. If it shows "cloud infrastructure" and you have three years of AWS experience but called it "cloud services" on your resume, that is a labeling issue you can fix in two minutes. For the deepest validation, paste your resume and a target job description into the full TalentTuner analysis — the itemised gap list will show you exactly which terms from the job description are missing or under-represented in your resume, weighted by how often they appear in the JD. That is a more precise signal than any static list can provide.
The keyword browser tells you what vocabulary the field uses. The full analysis tells you which terms this specific role needs, how much each one matters, and which gaps are hurting your score most. Both are useful; neither is a substitute for the other.
Abbreviation vs. Full-Form Keyword Preferences by Platform
The four ATS platforms modeled by TalentTuner handle keyword variants differently. The practical implication for your resume is to include both the abbreviated and full-form version of any credential or specialized term at least once.
| Term type | Workday preference | Oracle Taleo preference | Greenhouse / Lever preference | Recommended approach |
|---|---|---|---|---|
| Professional certifications (PMP, CPA, CFA, RN, CISSP) | Acronym often in structured credential field; relies on profile parsing | Full-form preferred in requisition matching; acronym alone may miss | Boolean search; recruiter queries both; include both forms | Write "Certified Public Accountant (CPA)" once, then abbreviation alone is sufficient in subsequent mentions |
| Technology platforms (Google Analytics, GA4, Salesforce, AWS) | Full product name preferred; Workday's skills taxonomy uses full names | Full name preferred; abbreviation may not match requisition criteria | Either; recruiter searches typically use product names | Use full product name (Salesforce CRM, Amazon Web Services) in skills section; abbreviation acceptable in bullets where context makes it clear |
| Methodologies (Agile, Scrum, Six Sigma, LEAN) | Methodology names are stable keywords; full name preferred | Requisition criteria list full names; use full names | Standard usage; full name preferred | Always spell out methodology names in full; they are queried by their full names across all four platforms |
| Regulatory frameworks (HIPAA, GDPR, SOX, PCI-DSS) | Acronym is the standard industry reference | Acronym standard; include full form once for clarity | Acronym sufficient; recruiter search queries use acronyms | Acronym is fine as the primary form; include full name once in the resume for completeness |
The single most reliable convention: on first mention, write the full form followed by the abbreviation in parentheses. After that, the abbreviation alone is safe. This satisfies both full-form and abbreviated search queries across all four platforms.
Related tools and methodology reading
- → Full TalentTuner resume analysis — applies TF-IDF weighting to your specific job description, not a static industry list
- → ATS resume format checker — verify your file is structurally readable before submitting
- → Methodology page — how TalentTuner weights keywords, what the score means, and where the model is accurate and where it is not
- → ATS match model — whitepaper section — TF-IDF mechanics, platform variance, and five-layer scoring detail
Ready to Optimize Your Resume?
Keywords are just the beginning. Get a complete ATS analysis of your resume with personalized recommendations.
Get Free Resume Analysis →Why ATS Keywords Matter for Your Resume
Applicant Tracking Systems (ATS) scan resumes for specific keywords before a human ever sees them. Without the right industry-specific keywords, even the most qualified candidates can be automatically rejected.
How to Use These Keywords Effectively
- Natural Integration: Don't just list keywords - integrate them naturally into your experience descriptions
- Match Job Descriptions: Align keywords with specific job postings you're targeting
- Industry Relevance: Focus on keywords most relevant to your specific role and industry
- Skills Balance: Include both technical skills and soft skills
Industries We Cover
Our keyword database covers major industries including technology, healthcare, finance, marketing, sales, human resources, engineering, education, retail, and project management. Each industry list is regularly updated to reflect current hiring trends and ATS requirements.