Job Market Change & Hiring Trend Analyzer

Compare recurring job listing datasets to detect openings, removals, role demand, location shifts, salary changes, remote-work trends and company hiring expansion or contraction.

Quick start

Start with this working example and replace the target values with your own:

{
  "currentItems": [
    {
      "id": "1",
      "title": "Senior AI Engineer",
      "company": "Acme",
      "location": "São Paulo",
      "salary": "R$20k",
      "remote": true,
      "url": "https://example.com/jobs/1"
    },
    {
      "id": "2",
      "title": "Data Engineer",
      "company": "Acme",
      "location": "Rio de Janeiro",
      "remote": false,
      "url": "https://example.com/jobs/2"
    },
    {
      "id": "3",
      "title": "AI Engineer",
      "company": "Beta",
      "location": "Remote",
      "remote": true,
      "url": "https://example.com/jobs/3"
    },
    {
      "id": "4",
      "title": "ML Engineer",
      "company": "Acme",
      "location": "São Paulo",
      "remote": true,
      "url": "https://example.com/jobs/4"
    }
  ],
  "previousItems": [
    {
      "id": "1",
      "title": "AI Engineer",
      "company": "Acme",
      "location": "São Paulo",
      "salary": "R$18k",
      "remote": true,
      "url": "https://example.com/jobs/1"
    },
    {
      "id": "5",
      "title": "Analyst",
      "company": "Beta",
      "location": "Remote",
      "remote": true,
      "url": "https://example.com/jobs/5"
    }
  ]
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• currentItems — Current job listings: Current normalized or raw job listing rows. Common title/company/location/url aliases are detected automatically.
• currentDatasetId — Current Apify Dataset ID: Optional Apify Dataset ID used when currentItems is not supplied.
• previousDatasetId — Previous Apify Dataset ID: Optional prior Dataset ID used instead of previousItems.
• maxItems — Maximum jobs: Maximum rows loaded from a Dataset source.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include dataset, report.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.008000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Detailed documentation

Compare recurring job listing datasets to detect new, removed and materially changed roles, salary/location shifts and market movement.

Why use this Actor

Raw job listings answer what is open. They do not directly answer which companies are accelerating hiring, which roles are rising, where demand is moving, or what changed since the last observation. This Actor converts supplied job rows into a compact intelligence layer for recruiting, workforce planning, competitive research, sales triggers and AI agents.

Key features

• Accept current job rows inline or from an Apify Dataset.
• Accept an optional prior snapshot for period-over-period intelligence.
• Normalize common title, company, location, salary, seniority, URL and remote-work aliases.
• Produce deterministic actions and evidence instead of opaque prose.
• Work across LinkedIn, Indeed and other upstream public-job datasets.
• Run data-first at 256 MB without a browser or external LLM.

Input

Provide current job listings inline through currentItems or reference an Apify Dataset with currentDatasetId. An optional previous snapshot can be supplied inline or through previousDatasetId to detect additions, removals and material changes. maxItems limits Dataset ingestion.

Output

The default Dataset receives one structured intelligence report, also stored as INTELLIGENCE_REPORT. It contains observed counts, deltas and decision fields appropriate to this product mode.

Example

Supply a current list of public job records plus the previous observation. The Actor normalizes the rows and reports the strongest hiring or market movement instead of returning another copy of the raw listings.

Use cases

• Competitive hiring surveillance.
• Workforce and location planning.
• Recruiting-market research.
• Sales and investment trigger generation.
• Scheduled company monitoring.
• Agent-ready hiring intelligence.

Pricing

Pay per completed intelligence report. The product deliberately prices the aggregated decision output rather than charging separately for every internal calculation.

Limitations

The Actor analyzes the data supplied to it. It does not authenticate to private job accounts, infer actual employee headcount, or claim that every open listing represents a unique approved hire. Missing upstream fields remain missing rather than being invented.

Reliability

All calculations are deterministic and reusable. If no current job rows are supplied, the run fails explicitly. This keeps scheduled monitoring honest, which is a surprisingly demanding standard for software.

Data interpretation

A listing is evidence of recruiting activity, not proof that a role will be filled. For that reason the output labels observations as signals and keeps counts, deltas and source rows conceptually separate. Snapshot comparison is most useful when upstream collection scope is held stable between runs.