# LinkedIn Jobs Hiring Demand & Talent Market API (`crocheted_poacher/hiring-demand-job-market-opportunity-intelligence`) Actor

Analyze public LinkedIn jobs to rank hiring demand, candidate opportunity, applicant competition, salary evidence, freshness, seniority, and workplace signals for APIs and AI agents.

- **URL**: https://apify.com/crocheted\_poacher/hiring-demand-job-market-opportunity-intelligence.md
- **Developed by:** [Benjamin Rogers](https://apify.com/crocheted_poacher) (community)
- **Categories:** Jobs, Business, Automation
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $50.00 / 1,000 hiring demand opportunities

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Hiring Demand & Job Market Opportunity Intelligence

Turn public LinkedIn job listings into ranked hiring-demand and candidate-opportunity signals for APIs, AI agents, workforce research, recruiting analytics, and market monitoring.

### What it adds

Instead of returning another raw job list, this Actor scores observed listing evidence:

- posting freshness
- applicant competition
- remote / hybrid flexibility
- disclosed salary evidence
- hiring expansion / urgency language
- role specialization and seniority

It returns separate **Hiring Demand** and **Candidate Opportunity** scores plus an overall rank.

### Example input

```json
{
  "keywords": "AI engineer",
  "location": "Australia",
  "maxInsights": 10,
  "minimumOpportunityScore": 55,
  "sourceJobs": 20,
  "datePosted": "pastWeek",
  "upstreamSpendGuardUsd": 0.25
}
```

### Evidence boundary

Scores are derived from public job-listing signals. They are not predictions of hiring outcomes and do not imply company budget, purchasing intent, financial strength, or guaranteed labor demand.

### Charging

Designed for Apify Pay Per Event:

- `hiring-opportunity` — launch price **US$0.05 per delivered ranked result**
- result storage and PPE charging use Apify's coupled delivery path
- filtered rows are not charged the custom event
- upstream spending is hard-capped

### Source

Current V1 source: `curious_coder/linkedin-jobs-scraper`, using public job listings with company-detail enrichment disabled to protect unit economics.

# Actor input Schema

## `keywords` (type: `string`):

Job title, skill, or search phrase.

## `location` (type: `string`):

City, region, country, or Remote.

## `maxInsights` (type: `integer`):

Maximum paid ranked job-market rows to return.

## `minimumOpportunityScore` (type: `integer`):

Only return listings scoring at or above this threshold.

## `sourceJobs` (type: `integer`):

Raw public LinkedIn jobs to inspect before ranking.

## `datePosted` (type: `string`):

Limit source listings by LinkedIn posting window.

## `upstreamSpendGuardUsd` (type: `number`):

Hard maximum USD allowed for the upstream LinkedIn jobs run.

## Actor input object example

```json
{
  "keywords": "AI engineer",
  "location": "Australia",
  "maxInsights": 10,
  "minimumOpportunityScore": 55,
  "sourceJobs": 20,
  "datePosted": "pastWeek",
  "upstreamSpendGuardUsd": 0.25
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("crocheted_poacher/hiring-demand-job-market-opportunity-intelligence").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("crocheted_poacher/hiring-demand-job-market-opportunity-intelligence").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call crocheted_poacher/hiring-demand-job-market-opportunity-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,crocheted_poacher/hiring-demand-job-market-opportunity-intelligence"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/K5Et7n6qstxXNG6q0/builds/cPB4HVcfRdXx3bzgE/openapi.json