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Data Jobs - ATS Resume Scorer (AU)

Know how your CV scores before you press apply. Grades any data, analytics, BI, ML or AI resume against 9 real Australian ATS rubrics in under 30 seconds.

What this is for

Australian recruiters and employers run their applicant pipelines through Applicant Tracking Systems - automated software that ranks incoming CVs against the JD before any human reads them. Each system has its own scoring algorithm, and what passes one rubric may fail another.

This Actor takes any CV (.docx, .pdf, .txt, .md - provided as URL, base64 or pasted text) plus a target JD, and returns one estimated score per rubric for the nine ATS systems most commonly used in Australia:

• SEEK TalentSearch® - used by 80%+ of AU recruiters
• LinkedIn Recruiter® - Talent Insights matching
• Workday® - federal government + ASX-100 default
• PageUp® - used by most AU universities and large corporates
• SmartRecruiters® - common at AU tech startups
• Oracle Taleo® - banking + insurance
• iCIMS® - multinationals operating in AU
• Greenhouse® - startup-friendly, increasingly common at scale-ups
• Lever® - VC-backed AU SaaS companies

Plus an aggregate "overall" market-weighted score, a semantic cosine similarity (how close the CV's meaning is to the JD), and a counterfactual amendment list ("add these N keywords for the largest predicted score uplift").

Estimates, not the vendor's algorithm. The score fields are named score_<vendor>_estimated because they are Data Lattice's independent approximations of each ATS's published parsing behaviour, recruiter- community testimony, and observed patterns. They are NOT produced by, endorsed by, or representative of the named vendors' actual scoring systems. Vendor names are used descriptively (nominative fair use) so you can match an estimate to the platform your target employer uses. Data Lattice has no affiliation with any vendor. See DISCLAIMER.md for the full statement.

How this helps you

What you get back

{
 "score_overall_estimated": 73.4, // market-weighted aggregate
 "score_seek_estimated": 78,
 "score_linkedin_estimated": 71,
 "score_workday_estimated": 64,
 "score_pageup_estimated": 81,
 ... // 5 more rubrics
 "semantic_cosine_pct": 67.2, // CV vs JD meaning similarity
 "amendments": [
 {"term": "data lineage", "uplift": +8.1, "reason": "JD mentions 4x; CV 0x"},
 {"term": "dbt", "uplift": +6.3, "reason": "..."},
 ...
 ],
 "cv_text_chars": 4127,
 "disclaimer": "Estimates only. ..."
}

Full schema in OUTPUT_SCHEMA.json.

Privacy

No candidate data is baked into the image. Every field comes from the input you provide. CV bytes are streamed to local TF-IDF / cosine math and discarded - nothing persists on Apify's servers beyond the standard 7-day dataset retention.

Pricing

$0.10 per CV scored (PAY_PER_EVENT, fired as cv_scored once per run). Job-seekers typically score 5-20 CVs over a search → less than $2 of usage end-to-end. Cheaper than ChatGPT subscription, far more specific.

Run it

{
"jd_text":"Senior Data Engineer (Python, Spark, AWS, Snowflake)...",
"cv_url":"https://example.com/my-cv.pdf",
"jd_title":"Senior Data Engineer",
"company":"Atlassian"
}

Or paste CV body inline:

{
"jd_text":"...",
"cv_text":"JANE DOE\nSenior Data Engineer\n10 years experience..."
}

Or upload a .docx as base64:

{
"jd_text":"...",
"cv_base64":"UEsDBBQABg...",
"cv_format":"docx"
}

Brand

Part of the Data Lattice portfolio. Pairs with the Skill Gap Analyser for actionable amendment lists.