Which ATS does the S&P 500 actually use? We indexed 503 careers pages and 9,260 live job postings.

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If you've ever applied to a job at a Fortune-class US company, your resume has been parsed by an Applicant Tracking System (ATS) before any human saw it. The system extracts your work history, education, and skills, then scores you against the job description.

The question that gets asked constantly — and never gets a real, data-backed answer — is:

"Which ATS is actually filtering me out, and how do I tailor my resume to it specifically?"

Most "ATS market share" content online quotes vendor press releases or unverifiable third-party reports. We wanted ground truth: which software does each S&P 500 company actually deploy, and what do their job postings actually ask for?

So we built it from scratch.

#How we did it

1. Company list. We pulled the current S&P 500 constituents from Wikipedia's list (503 rows due to dual-class shares).
2. Domain resolution. For each company we resolved the canonical web domain via a curated lookup for the 100 mega-caps and Clearbit autocomplete with name-token validation for the rest.
3. Careers page discovery. We tried a battery of likely URL patterns (/careers, /jobs, careers.{domain}, jobs.{domain}, etc.) and followed all redirects to capture the final landing page.
4. ATS classification. We pattern-matched URL hostnames and embedded HTML/script signatures against ~25 known ATS vendors. When the static landing page didn't match, we followed iframes and "view jobs" links one level deeper. As a last resort, we probed each company's likely tenant slug against the public APIs of Greenhouse, Lever, Ashby, and Workable — a 200 response with a valid jobs payload is a definitive identification.
5. Job-posting layer. For the 105 companies we found on Greenhouse / Lever / Workday public APIs, we pulled every active posting via their first-party JSON endpoints. No LinkedIn or Indeed scraping — only first-party career data the companies themselves publish.
6. Analysis. Standard pandas pipeline; role-family classification via regex on job titles; "years of experience," degree requirements, location mix, and keyword frequency derived by string match against the description bodies. Workday's public listings are title-only, so every description-level share (degree, work mode, years of experience, keywords) is computed only on distinct postings that have a full description: 2,098 postings from the 28 companies whose Greenhouse or Lever boards publish full descriptions, with duplicate reposts counted once and company boilerplate (about-us, benefits and EEO text) removed before counting keywords.

Detection rate: 57.3% (288/503). The 215 undetected companies are mostly SPA careers sites that load the ATS via async JavaScript — a headless-browser run would push detection higher; we plan to follow up.

Raw data + replication code at the bottom of this post.

#Finding 1: The ATS landscape of the S&P 500

Among the 288 companies where we could identify the ATS:

The table below pairs each ATS with the resume-tailoring advice that's actually specific to that ATS's parser — not generic "use keywords" advice, but what each parser breaks on or rewards. The advice is informed by the live job postings in our dataset plus public documentation each vendor publishes on resume parsing.

Read "30.2% (87)" as "30.2% of the 288 detected companies, or 87 companies total."

A few notes on these numbers:

• Phenom People is a candidate-experience and recruitment-marketing platform that is frequently layered on top of an underlying ATS (often Workday or Oracle). When we detected Phenom from the careers site, we couldn't always see what was behind it — the real Workday + Oracle numbers are likely modestly higher than what we report.
• Greenhouse in the S&P 500 is concentrated in younger, tech-forward companies that joined the index relatively recently (Airbnb, Datadog, Palo Alto Networks, Block, Veeva Systems). Greenhouse's actual market share across all US companies is much higher than 8% — it just doesn't dominate the legacy enterprise.
• Oracle's combined footprint (HCM Cloud + Taleo) is 38 companies, about 13% — closer to Workday than people usually realize.

The headline: when you apply to a Fortune-class US company, you have roughly a 1-in-3 chance you're hitting Workday and a 1-in-2 chance you're hitting one of the "enterprise big three." If you've optimized your resume for "an ATS" in the abstract, you've probably optimized it for the wrong one.

Search any of the 503 companies in our standalone ATS Finder. For 17 companies where we collected enough live postings — Datadog, Airbnb, Palantir, Coinbase, and others — we've published full hiring breakdowns with role mix, in-demand skills, and experience requirements pulled from real job descriptions.

#Finding 2: ATS varies dramatically by sector

The same resume strategy doesn't work equally well in every industry, because the parser behind the form is different. Dominant ATS by GICS sector, among detected companies:

Interpretation:

• If you're applying to utilities or energy companies, your resume is going through SAP SuccessFactors more often than not. SuccessFactors is notoriously strict on tables, multi-column layouts, and atypical section headers — keep both very plain. (See our ATS formatting guide.)
• Health Care has the highest single-vendor concentration of any sector (46% Workday). Tailoring once for Workday gets you most of the way through your applications. We wrote a Workday-specific resume guide for this exact reason.
• Information Technology is the most fragmented — Workday, Greenhouse, Phenom, iCIMS, and SuccessFactors all hold meaningful share. Tech job seekers benefit most from checking the ATS per application.

#Finding 3: Most JDs don't explicitly require a degree, but about 1 in 10 do

Across 1,149 distinct job descriptions with full content in the 8 main role families (from the 21 companies in those families whose Greenhouse or Lever boards publish full descriptions), we measured how often a posting attaches an explicit "required" or "must have" to a degree phrase, and how often it mentions a degree at all:

The honest read: about 1 in 10 postings with a full description explicitly require a degree, and roughly a third mention one somewhere. Most of those mentions sit in a list of qualifications without the word "required" attached. Finance and data roles lean on degrees most; sales and customer support least.

This doesn't mean ATSes don't auto-filter on degree — many do, and many recruiters add the filter manually as a screening rule even when the JD doesn't say so. But if you've been self-rejecting because you assume every posting demands a degree, the written demand is narrower than that: for most roles, the skills and experience you can show still carry more of the decision than the degree line.

#Finding 4: Median experience demands by role

For postings that state an explicit "N+ years of experience" (317 distinct postings across the 8 main role families), the median demand by role family:

Marketing demanding 7 years at the median — more than engineering — is the headline. The S&P 500 hires senior marketing roles disproportionately on Greenhouse and Lever (early-career marketing is typically internal pipelines or staffing agencies, not posted on public boards), which skews the visible postings older. Several of these n's are small (13 to 27), so read the non-engineering medians as directional.

#Finding 5: Remote/hybrid by role

Among the 1,149 distinct postings with a full description in the 8 main role families, the share whose description explicitly states each work model:

Note: a posting can state more than one model (for example "hybrid, or remote for the right candidate"), so a row can add up to more than 100%. Many postings also leave the model to the location field, so read these as "the share of postings that flag this model in the description".

#Finding 6: Tech spotlight — what S&P 500 engineering jobs actually ask for

This is the section job seekers should print and tape to their wall.

Across 492 distinct engineering postings with a full description (from 16 companies, after removing company boilerplate), the share that mention each technology or phrase:

Hard skills:

Counter-intuitive observations:

• AWS leads the clouds, but not by a landslide. AWS appears in 18.1% of engineering JDs, Azure in 12.4% and GCP in 7.5%: about 1.5× Azure and 2.4× GCP.
• AI is in the JDs now, but it isn't the core of them. "AI / LLMs" appears in 13.2% of engineering postings, roughly level with Kubernetes and CI/CD, and well behind Python (30.9%) and Java (21.3%). If your resume leads with LLM keywords for a non-AI-specialist role, you're still optimizing for a minority of postings.
• JavaScript and React are under-represented compared to the impression you'd get from r/cscareerquestions: neither makes the top 17 above. The S&P 500 engineering org in this sample is mostly backend (Python/Java) + cloud (AWS) + infrastructure (CI/CD, Kubernetes, Linux).
• Python leads Java (30.9% vs 21.3%), and the two travel together: 70.5% of the postings that mention Java also mention Python. Java is far from dead at the Fortune 500.

Buzzwords / soft language:

If you're mirroring JD language on your resume (which is the standard ATS-keyword optimization advice — and it's correct, see our keyword guide), those three are the highest-leverage soft-language ones to consider including. Cross-functional collaboration and stakeholder management each show up in about 1 of every 4 S&P 500 engineering postings with a description.

#What this means for your resume

Three practical takeaways:

1. Before you apply, identify the ATS. Look at the URL of the apply form. myworkdayjobs.com is Workday, boards.greenhouse.io is Greenhouse, *.icims.com is iCIMS, *.taleo.net is Oracle Taleo, *.successfactors.com is SAP, *.smartrecruiters.com is SmartRecruiters. Each parses your resume differently. Workday and SAP SuccessFactors penalize tables and multi-column layouts hardest. Greenhouse is the most forgiving. Tailor your resume file format accordingly.
2. Mirror the JD's exact phrasing. Most ATSes use a hybrid of substring matching and semantic similarity. If the JD says "stakeholder management" and you wrote "managed stakeholders," some systems score those materially differently. Use the keyword extractor in our ATS resume optimizer to pull the JD's exact terms.
3. Don't over-rotate on AI buzzwords for non-AI roles. The data shows S&P 500 engineering postings still ask for Python, Java and AWS more than for AI or LLMs (13.2% of engineering postings with a description). Lead your resume with what JDs actually ask for, then mention AI/ML if it's in your background.

#Raw data and reproduce yourself

We're publishing the full dataset and methodology so you can verify, replicate, or correct anything in this post:

• Companies + ATS classification (503 rows, 70 KB): download CSV
• Job postings (9,260 rows, 19 MB): download CSV
• Findings JSON (machine-readable output of the original June analysis): download JSON. Its company-level ATS tables are current; its per-role degree, work-mode, experience and keyword tables used the old all-postings denominator and are superseded by the tables above.
• Charts as PNGs: 01 · 02. The degree, experience, work-mode and keyword charts are being redrawn with the corrected figures.

Methodology corrections welcome — open an issue or drop us a note and we'll update the post with a credit.

#FAQ

Frequently asked questions

How was the ATS detected for each company?

We fetched the careers page, followed redirects, parsed embedded iframes/scripts, and ran roughly 30 regex patterns against the resulting URL plus body to identify well-known ATS vendor signatures. For companies that didn't match on the page, we probed the public board APIs of Greenhouse, Lever, Ashby, and Workable with likely tenant slugs — a positive 200 response with a valid jobs payload is a definitive identification.

Why is your detection rate only 57%?

The remaining 43% use single-page-app (SPA) careers sites that load the ATS via async JavaScript. We ran static HTML fetches only. A headless-browser pass (Playwright) would push detection significantly higher; we plan a follow-up that does this.

Why is your job-posting count lower than the company count would imply?

We pulled from public board APIs only. Workday's list endpoint returns more than 6,000 live postings across the 87 Workday companies we found, but the title-only payload meant we couldn't analyze description content for those. Greenhouse and Lever return full content: 2,905 postings, or 2,098 distinct descriptions once reposts of the same description are counted once, from 28 companies. Those full-text postings power every keyword, years-of-experience, degree and work-mode figure; the full set of 9,260 is used only for role-family counts and top-company lists.

Is this representative of the whole US job market?

No. The S&P 500 is the largest publicly listed US companies. Smaller employers and startups tilt heavily toward Greenhouse, Lever, Ashby, and Workable and will produce different ATS share numbers. We chose the S&P 500 because it's a defined, replicable universe with a fixed roster.

How do I use this to actually land more interviews?

Pick the ATS you're applying through, tailor your resume to it (our free ATS audit tells you exactly which JD keywords you're missing), and mirror the language that the JD actually uses — which the tables above quantify.