# Greenhouse & Lever Hiring Signals Scraper (`happyfhantum/career-site-hiring-signals-scraper`) Actor Turn public job boards into hiring signals with department, seniority, and tech-stack tags. - **URL**: https://apify.com/happyfhantum/career-site-hiring-signals-scraper.md - **Developed by:** [Kelsey Todd](https://apify.com/happyfhantum) (community) - **Categories:** Jobs, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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 ## Greenhouse & Lever Hiring Signals Scraper Turn public Greenhouse and Lever job boards into a clean hiring-signal dataset instead of a raw job export. ### What it does - Pulls jobs directly from public Greenhouse and Lever board APIs. - Normalizes titles, locations, team names, commitment, and apply URLs. - Infers department and seniority from the role title and description. - Tags common stack and buying signals like `power_bi`, `microsoft_fabric`, `azure`, and `finance_systems`. - Adds board-level context such as total jobs, finance job count, data job count, engineering job count, and recent posting volume. - Adds `job_signal_headline`, `workflow_pain_summary`, `automation_hint`, and `workflow_pressure_score` to make rows easier to act on. - Writes a `HIRING_SIGNAL_BRIEF.md` artifact summarizing the strongest boards in the run. - Produces flat output that is easy to export into CSV, Sheets, Airtable, or downstream lead-scoring workflows. ### Best fit - agencies tracking hiring signals across target accounts - sales teams looking for timing signals from finance or data hiring - analysts mapping job boards into structured ICP signals - RevOps teams enriching company-level hiring data without building custom parsers # Actor input Schema ## `greenhouseUrls` (type: `array`): Paste Greenhouse board URLs such as https://boards.greenhouse.io/openai or https://boards.greenhouse.io/embed/job_board?for=openai ## `leverUrls` (type: `array`): Paste Lever board URLs such as https://jobs.lever.co/netlify ## `maxJobsPerBoard` (type: `integer`): Cap how many jobs to return for each board. ## `includeDescriptions` (type: `boolean`): Include description excerpts and use them for department and stack-tag inference. ## `requestTimeoutSecs` (type: `integer`): Maximum time to wait for a board API request before skipping it. ## Actor input object example ```json { "greenhouseUrls": [ "https://boards.greenhouse.io/openai" ], "maxJobsPerBoard": 100, "includeDescriptions": true, "requestTimeoutSecs": 30 } ```` # 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 = { "greenhouseUrls": [ "https://boards.greenhouse.io/openai" ] }; // Run the Actor and wait for it to finish const run = await client.actor("happyfhantum/career-site-hiring-signals-scraper").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 = { "greenhouseUrls": ["https://boards.greenhouse.io/openai"] } # Run the Actor and wait for it to finish run = client.actor("happyfhantum/career-site-hiring-signals-scraper").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 '{ "greenhouseUrls": [ "https://boards.greenhouse.io/openai" ] }' | apify call happyfhantum/career-site-hiring-signals-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=happyfhantum/career-site-hiring-signals-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Greenhouse & Lever Hiring Signals Scraper", "description": "Turn public job boards into hiring signals with department, seniority, and tech-stack tags.", "version": "0.1", "x-build-id": "mLPtyaI9shOVWluci" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/happyfhantum~career-site-hiring-signals-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-happyfhantum-career-site-hiring-signals-scraper", "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/happyfhantum~career-site-hiring-signals-scraper/runs": { "post": { "operationId": "runs-sync-happyfhantum-career-site-hiring-signals-scraper", "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/happyfhantum~career-site-hiring-signals-scraper/run-sync": { "post": { "operationId": "run-sync-happyfhantum-career-site-hiring-signals-scraper", "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", "properties": { "greenhouseUrls": { "title": "Greenhouse board URLs", "type": "array", "description": "Paste Greenhouse board URLs such as https://boards.greenhouse.io/openai or https://boards.greenhouse.io/embed/job_board?for=openai", "items": { "type": "string" } }, "leverUrls": { "title": "Lever board URLs", "type": "array", "description": "Paste Lever board URLs such as https://jobs.lever.co/netlify", "items": { "type": "string" } }, "maxJobsPerBoard": { "title": "Max jobs per board", "minimum": 1, "maximum": 500, "type": "integer", "description": "Cap how many jobs to return for each board.", "default": 100 }, "includeDescriptions": { "title": "Include descriptions", "type": "boolean", "description": "Include description excerpts and use them for department and stack-tag inference.", "default": true }, "requestTimeoutSecs": { "title": "Request timeout (seconds)", "minimum": 5, "maximum": 60, "type": "integer", "description": "Maximum time to wait for a board API request before skipping it.", "default": 30 } } }, "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 } } } } } } } } } } ```