Job Title to SOC Code Mapper with O*NET Data

Job Title to SOC Code Mapper turns messy job titles like "RN - Med/Surg Nights $5K Sign-On Bonus" or "Sr. Backend Engineer (Remote)" into a standard occupation code: the SOC code and O*NET-SOC code, the occupation name, major group, job zone and seniority. Give it job descriptions too, and it also tags the software and technology skills named in each posting (Python, AWS, Salesforce, Epic and 8,000+ more) with O*NET data, marking "hot" and in-demand technologies. Each row has a confidence score, the matched title and up to 3 alternatives. Click Start with the prefilled example to map four job titles in a few seconds.

Best-match classification, not an official SOC determination. Results are for analytics, research and data cleaning. They are not legal, wage or immigration advice. Check the code yourself before using it in a visa, prevailing-wage or government filing.

What does the Job Title to SOC Code Mapper do?

• ✅ Maps each job title to the 2018 SOC code (e.g. 15-1252) and the more detailed O*NET-SOC code (e.g. 15-1252.00), with the occupation name and a link to its O*NET OnLine page.
• ✅ Cleans titles first: removes pay, bonus, shift, location and "remote/hybrid" noise, expands abbreviations (RN, LPN, CNA, CDL, Sr., Mgr., Dev, QA and more) and splits titles like "Medical Biller / Coder" or "Engineer - Backend".
• ✅ Matches against 67,000+ real job titles from O*NET's alternate and reported titles: exact match first, then exact match ignoring the level word, then a fixed table of very common titles, then fuzzy word matching. The matchMethod field tells you which one was used.
• ✅ Adds the major group (e.g. "Healthcare Practitioners and Technical"), the job zone (1-5, how much preparation the job needs) and seniority (intern, junior/entry, mid, senior, lead/principal, manager, director, vice president, C-level).
• ✅ Uses the job description to break close ties between occupations, when you give one.
• ✅ Extracts technology skills from descriptions with O*NET's technology list. Short words that are also ordinary English (Python, Excel, Slack, Outlook, Epic, React) only count when the same sentence also names another tool (for some, a phrase like "experience with" is enough). In our tests on 900 Wikipedia intros and 450 other non-job texts, only 2 texts got a skill, both correct (Perl and Google Ads), and 8 non-tech job postings got none.
• ✅ Takes a plain list of titles, job objects, or a whole Apify dataset from a LinkedIn, Indeed or other job scraper. Title, description and job ID fields are found automatically.

Who is it for?

• Job boards and recruiting tools that need a clean occupation filter or taxonomy on scraped listings.
• Labor-market and HR analysts grouping postings by occupation and seniority, or counting demand for skills.
• Data teams enriching job datasets before joining them with wage or employment statistics that use SOC codes.
• Researchers and students coding job titles into a standard classification at scale.

How to map job titles to SOC codes

1. Click Start with the prefilled example to see the output.
2. Paste your own titles into Job titles, one per line, or add Jobs with your own ID, title and description.
3. Or pick the dataset of a job scraper run. Leave the field names empty to auto-detect the title and description.
4. Keep Extract technology skills on if you pass descriptions.
5. Download as JSON, CSV or Excel. Sort by confidence and check the low ones against alternatives.

Input example

{
  "titles": [
    "Senior Software Engineer - Backend (Remote, US)",
    "RN - Med/Surg Nights $5K Sign-On Bonus",
    "CDL-A Truck Driver (Home Weekly)",
    "Line Cook"
  ],
  "extractTechnologies": true
}

Output example

Real output from the prefill run on 2026-10-25 (first row in full, the rest shortened):

[
  {
    "itemNo": 1,
    "ref": null,
    "inputTitle": "Senior Software Engineer - Backend (Remote, US)",
    "titleCleaned": "Senior Software Engineer - Backend",
    "matchMethod": "exact-ignoring-level-segment",
    "onetSocCode": "15-1252.00",
    "socCode": "15-1252",
    "occupationTitle": "Software Developers",
    "matchedTitle": "Software Engineer",
    "confidence": 0.88,
    "majorGroup": "15-0000",
    "majorGroupName": "Computer and Mathematical",
    "jobZone": 4,
    "jobZoneMeaning": "Considerable preparation needed",
    "alternatives": [],
    "onetUrl": "https://www.onetonline.org/link/summary/15-1252.00",
    "seniority": "senior",
    "technologies": [],
    "technologyCount": 0,
    "hotTechnologies": [],
    "usedDescription": false,
    "dataVersion": "O*NET 31.0 Database",
    "checkedAt": "2026-10-25T18:15:56+00:00",
    "disclaimer": "Automated best-match classification from public O*NET data, for analytics and research. It is not an official SOC determination and not legal or immigration advice; verify codes before using them in wage, visa or government filings.",
    "attribution": "Includes information from the O*NET 31.0 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. Ultimate Foresight has modified all or some of this information (title normalization, matching and skill aliases). USDOL/ETA has not approved, endorsed, or tested these modifications."
  },
  {
    "itemNo": 2,
    "inputTitle": "RN - Med/Surg Nights $5K Sign-On Bonus",
    "matchMethod": "exact-segment",
    "onetSocCode": "29-1141.00",
    "socCode": "29-1141",
    "occupationTitle": "Registered Nurses",
    "confidence": 0.88,
    "majorGroupName": "Healthcare Practitioners and Technical",
    "jobZone": 4,
    "seniority": "unspecified"
  },
  {
    "itemNo": 3,
    "inputTitle": "CDL-A Truck Driver (Home Weekly)",
    "matchMethod": "exact",
    "onetSocCode": "53-3032.00",
    "socCode": "53-3032",
    "occupationTitle": "Heavy and Tractor-Trailer Truck Drivers",
    "confidence": 0.97,
    "majorGroupName": "Transportation and Material Moving",
    "jobZone": 2,
    "seniority": "unspecified"
  },
  {
    "itemNo": 4,
    "inputTitle": "Line Cook",
    "matchMethod": "common-title",
    "onetSocCode": "35-2014.00",
    "socCode": "35-2014",
    "occupationTitle": "Cooks, Restaurant",
    "confidence": 0.9,
    "majorGroupName": "Food Preparation and Serving Related",
    "jobZone": 2,
    "seniority": "unspecified"
  }
]

With a description, the same run also returns skills. A real test run with the posting "Build pipelines with Python, SQL and Snowflake. Experience with Airflow, dbt and AWS is a plus." returned ["Python", "SQL", "Snowflake", "Airflow", "AWS"] for "Data Engineer II (Hybrid)".

How much does it cost to map job titles?

Pay per result: $0.002 per job title, plus a $0.00005 start fee per run and small Apify platform usage paid by you (the 4-title prefill run used about $0.0003 of platform usage). 1,000 titles cost about $2.00 and 10,000 about $20.00, plus platform usage. Use Max items to cap a run.

Use it through the API, integrations or AI agents

• Run and get results in one call: https://api.apify.com/v2/acts/ultimatemc~job-title-soc-code-mapper/run-sync-get-dataset-items
• MCP for AI agents: https://mcp.apify.com?tools=ultimatemc/job-title-soc-code-mapper
• Chain it after a job scraper: pass that run's dataset ID, or call it from a webhook when the scraper finishes.

Data source and attribution

This Actor includes information from the O*NET 31.0 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. ONET® is a trademark of USDOL/ETA. Ultimate Foresight has modified all or some of this information (title normalization, matching and skill aliases). USDOL/ETA has not approved, endorsed, or tested these modifications. The same notice is in every output row. Ultimate Foresight is not affiliated with USDOL/ETA or the ONET program.

How the matching works

Everything runs on a fixed copy of the O*NET data bundled with the Actor. The title is cleaned, then matched by exact title, exact title without the level word, a short table of very common titles, and finally by word overlap weighted so rare words count more and the last noun (e.g. "nurse", "driver") counts extra. Ties go to broader, more common occupations, or to the one whose description best fits the job description you passed. No AI and no external calls, so the same input always gives the same output.

FAQ

Is this the official SOC code for my job?

No. It's an automated best match. Official classification depends on the actual duties, and agencies decide codes for their own filings. Use confidence and alternatives to review.

What's the difference between socCode and onetSocCode?

socCode is the 2018 Standard Occupational Classification code used by BLS and other agencies. onetSocCode adds O*NET's two-digit detail (e.g. 29-1141.01 Acute Care Nurses inside 29-1141 Registered Nurses).

What happens with titles it can't map?

They're kept with matchMethod "none", an empty code and confidence 0, so nothing is silently dropped. Joke titles like "Ninja Rockstar Wizard" usually end up here.

Does it work for non-English titles?

No. It's built for English titles from US-style job postings.

Is it AI?

No. Deterministic rules and public data, so results are repeatable and auditable.

Limitations

• Titles alone can be vague ("Associate", "Specialist", "Analyst"). Pass a description for better results and check low-confidence rows.
• English only, and the data is US-focused.
• Technology skills come from O*NET's list; very new or niche tools may be missing.
• O*NET data version 31.0. Occupation codes follow the 2018 SOC.

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Wrong code or missed skill? Open an issue on the Issues tab with the title (and description), and we'll review the match.