# Job Market Intelligence API (`second_coming/job-market-intel`) Actor Scrape and analyze job listings from Indeed, LinkedIn, and Glassdoor. Get structured data on job titles, companies, salaries, skills, and location-based salary analysis. - **URL**: https://apify.com/second\_coming/job-market-intel.md - **Developed by:** [Richard P](https://apify.com/second_coming) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Job Market Intelligence API Scrape and analyze job listings from Indeed, LinkedIn, and Glassdoor. Get structured data on job titles, companies, salaries, skills, and location-based salary analysis. ### Features - **Multi-source search** — Query Indeed, LinkedIn, and Glassdoor simultaneously - **Keyword & location filtering** — Search by job title/keywords and location - **Salary parsing** — Extract and normalize salary ranges (hourly, yearly, etc.) to yearly equivalents - **Skill extraction** — Automatically detect ~150+ known skills in job titles and descriptions - **Deduplication** — Remove duplicate listings by company + title + location - **Salary analysis** — Compute averages, min/max, and breakdowns by location and keyword - **Configurable output** — Control max results and enable/disable salary analysis - **Webhook support** — Optional notification on run completion ### Input | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `keywords` | `array[string]` | ✅ | — | Job search keywords (e.g., `["software engineer"]`) | | `location` | `string` | ❌ | — | Job location filter (e.g., `"remote"`, `"New York"`) | | `sources` | `array[string]` | ❌ | `["indeed","linkedin","glassdoor"]` | Job boards to search | | `maxResults` | `integer` | ❌ | `20` | Max listings per source per keyword | | `includeSalaryAnalysis` | `boolean` | ❌ | `true` | Compute salary statistics in output | | `webhookUrl` | `string` | ❌ | — | URL for completion notification | #### Example Input ```json { "keywords": ["software engineer", "data scientist"], "location": "remote", "sources": ["indeed", "glassdoor"], "maxResults": 10, "includeSalaryAnalysis": true } ```` ### Output Each job listing contains: | Field | Type | Description | |-------|------|-------------| | `timestamp` | `string` | ISO 8601 timestamp of when the listing was scraped | | `source` | `string` | Origin job board (`indeed`, `linkedin`, `glassdoor`) | | `title` | `string` | Job title | | `company` | `string` | Employer name | | `location` | `string` | Job location | | `salary` | `string` | Raw salary text | | `salaryMin` | `number` | Minimum salary value | | `salaryMax` | `number` | Maximum salary value | | `salaryType` | `string` | Pay type (`yearly`, `hourly`, `monthly`, etc.) | | `description` | `string` | Job description snippet | | `postingDate` | `string` | Relative or absolute posting date | | `url` | `string` | Link to the original job listing | | `skills` | `array[string]` | Detected skills from title and description | | `jobType` | `string` | Employment type (e.g., Full-time) | When `includeSalaryAnalysis` is enabled, a special analysis entry is appended with fields for salary statistics by keyword, location, and top skills. ### Quick Start ```bash ## Install dependencies python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt ## Run locally APIFY_ACTOR_PYTHON_VENV_PATH=.venv apify run --purge ## Deploy to Apify apify push ## Run on Apify cloud apify call job-market-intel --input '{"keywords":["software engineer"],"location":"remote","sources":["indeed"],"maxResults":5}' ``` ### Notes - **Indeed** — Scrapes public job search results. Works with standard HTTP requests. - **Glassdoor** — Scrapes public job search results. Works with standard HTTP requests. - **LinkedIn** — LinkedIn aggressively blocks automated HTTP scraping. Results may be limited or empty without using a browser-based crawler. - Salary analysis normalizes all amounts to yearly equivalents for consistent comparison. - Rate limiting and respectful scraping practices are implemented — delays vary by source. # Actor input Schema ## `keywords` (type: `array`): Job search keywords (e.g., software engineer, data scientist) ## `location` (type: `string`): Job location filter (optional, e.g., remote, New York, London) ## `sources` (type: `array`): Job sources to search: 'indeed' uses RemoteOK API, 'glassdoor' uses USAJobs API, 'linkedin' (unavailable from cloud). Defaults to all available if empty. ## `maxResults` (type: `integer`): Maximum number of job listings to return per source ## `includeSalaryAnalysis` (type: `boolean`): Compute salary averages and ranges in the output ## `webhookUrl` (type: `string`): Optional URL to receive a notification when the run completes ## Actor input object example ```json { "keywords": [ "software engineer" ], "location": "remote", "sources": [ "indeed" ], "maxResults": 20, "includeSalaryAnalysis": true } ``` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "keywords": [ "software engineer" ], "location": "remote", "sources": [ "indeed" ] }; // Run the Actor and wait for it to finish const run = await client.actor("second_coming/job-market-intel").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "keywords": ["software engineer"], "location": "remote", "sources": ["indeed"], } # Run the Actor and wait for it to finish run = client.actor("second_coming/job-market-intel").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "keywords": [ "software engineer" ], "location": "remote", "sources": [ "indeed" ] }' | apify call second_coming/job-market-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=second_coming/job-market-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Job Market Intelligence API", "description": "Scrape and analyze job listings from Indeed, LinkedIn, and Glassdoor. Get structured data on job titles, companies, salaries, skills, and location-based salary analysis.", "version": "0.0", "x-build-id": "CXyZhT5dSokyfNWxw" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/second_coming~job-market-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-second_coming-job-market-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/second_coming~job-market-intel/runs": { "post": { "operationId": "runs-sync-second_coming-job-market-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/second_coming~job-market-intel/run-sync": { "post": { "operationId": "run-sync-second_coming-job-market-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "keywords" ], "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "Job search keywords (e.g., software engineer, data scientist)" }, "location": { "title": "Location", "type": "string", "description": "Job location filter (optional, e.g., remote, New York, London)" }, "sources": { "title": "Sources", "type": "array", "description": "Job sources to search: 'indeed' uses RemoteOK API, 'glassdoor' uses USAJobs API, 'linkedin' (unavailable from cloud). Defaults to all available if empty." }, "maxResults": { "title": "Max Results", "type": "integer", "description": "Maximum number of job listings to return per source", "default": 20 }, "includeSalaryAnalysis": { "title": "Include Salary Analysis", "type": "boolean", "description": "Compute salary averages and ranges in the output", "default": true }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "Optional URL to receive a notification when the run completes" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```