JOB POSTING DATA METHODOLOGY

A data methodology,visible in every record.

Understand how public employer job pages become normalized blue-collar job data. Source evidence is preserved while AI assists with classification, normalization and selected enrichment.

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SOURCE → RECORD08 AUG 2026

METHOD / SIX STAGESA clear path from page to record.

01 · SourcePublic page

Employer career sites and employer-controlled vacancy pages

02 · ObserveTimestamp

Record first and most recent successful observations

03 · ExtractSource values

Preserve titles, employer and source context

04 · EnhanceAI-assisted

Classify and enrich selected fields without generating listings

05 · NormalizeShared schema

Create comparable occupation, location and record fields

SCOPE DEFINEDSOURCE CONTEXT RETAINED

01DATED

Snapshot attached

02DEFINED

Fields and scope explained

03TRACEABLE

Source context retained

04HONEST

Limits stay visible

01 / WHY IT MATTERS

FROM SOURCE PAGE TO USABLE RECORD

Transformation should add structure, not hide the evidence.

A buyer should be able to tell what came from the source, what was normalized, what was inferred with AI assistance and when the listing was observed. The method is part of the product contract.

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02 / EVALUATION CRITERIA

WHAT BUYERS SHOULD EXPECT

Evidence that makes the data easier to trust.

Discover and observe

Begin with public employer-controlled job pages and record when a listing is successfully observed.

Extract and preserve

Capture useful source values and retain the route needed to inspect the original public listing.

Classify and normalize

Use deterministic rules and AI assistance to make occupations, locations and selected fields more comparable.

Disclose and verify

Ship field definitions, scope, snapshot dates, lifecycle rules and known limitations with the data.

03 / THE DETAILS

METHOD / SIX STAGES

A clear path from page to record.

ITEMVALUEDEFINITION

01 · SourcePublic page

Employer career sites and employer-controlled vacancy pages

02 · ObserveTimestamp

Record first and most recent successful observations

03 · ExtractSource values

Preserve titles, employer and source context

04 · EnhanceAI-assisted

Classify and enrich selected fields without generating listings

05 · NormalizeShared schema

Create comparable occupation, location and record fields

06 · DiscloseBuyer context

Attach scope, definitions, cadence and known limitations

Evaluate this with real records. Include your market, occupations and intended workflow.

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04 / NEXT ROUTES

FROM EVIDENCE TO EVALUATION

Continue with the delivery that fits your workflow.

JOB POSTING API

Query normalized records

Explore structured job data for recruitment products, alerts and software workflows.

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BULK JOB DATA

Load a defined extract

Explore larger deliveries for warehouses, models, workforce analytics and research.

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05 / FAQ

BUYER QUESTIONS

What to confirm before you use the data.

Ask about a specific market, field or delivery requirement.

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01Does AI create or rewrite the job listings?

No. AI assists with classification, normalization and selected field enrichment. The vacancies originate from public employer sources, and source context is retained.

02How are duplicate records handled?

Source identity and repeated observations provide the basis for deduplication and lifecycle logic. Exact matching rules should be documented for the commercial dataset rather than inferred from this overview.

03What do first seen and last seen mean?

They describe successful source observations. The precise cadence and the rule for deciding that a listing is no longer active should be included in the delivery methodology.

04Can buyers inspect normalized and original values?

That is the intended trust model. Ask for a sample field dictionary to confirm which source values and normalized fields are included in the delivery you are evaluating.

06 / NEXT STEP

START WITH A SCOPED SAMPLE

Bring us the question your data needs to answer.

Tell us your market, occupations and workflow. We will confirm whether the current coverage can support a useful evaluation before asking you to sit through a generic sales call.

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EMAIL THE DATA TEAM

MARKET: Country or region
OCCUPATIONS: Trades or frontline roles
WORKFLOW: Product, analysis or research
QUESTION: What the records need to answer

[email protected]