# ATS Job Scraper — Greenhouse & Lever Boards (`arbogaste/hiring-intent-buying-signals`) Actor Scrape jobs straight from company ATS boards (Greenhouse, Lever, Ashby) — source-direct, so no LinkedIn/Indeed blocking, no CAPTCHAs, no proxies. 116 verified company boards included. Three modes: job listings, B2B hiring-intent signals, or technographics (which tools a company uses). - **URL**: https://apify.com/arbogaste/hiring-intent-buying-signals.md - **Developed by:** [Samuele Guarnaccia](https://apify.com/arbogaste) (community) - **Categories:** Automation, Developer tools, Jobs - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 job listings 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/platform/actors/running/actors-in-store#pay-per-event ## 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Hiring-Intent Buying Signals Turn public ATS job boards (Greenhouse, Lever) into **B2B buying signals**. When a company opens several roles in an area, it's investing there — a lead for whoever sells into that area. ### Who buys this B2B sales teams, SDRs, staffing/recruiting agencies, cold-email marketers, consultants. Hiring intent is a proven paid category (Apollo/Saleshandy $49–79/mo, IntenTrack $500/mo, Bombora/6sense $15–50k/yr). ### What it does 1. Reads each company's public board (Greenhouse `content=true`, or Lever) — no auth. 2. Keeps roles matching your `roleKeywords` (word-boundary match, so "ai"/"ml" don't hit "maintenance"). 3. Counts recent matching roles; if ≥ `minOpenings`, emits a **buying signal** with the top department. ### Works out of the box No need to know ATS board slugs: pick a **companyPreset** (`ai`, `fintech`, `devtools`, `saas`, `marketplace`, `health`, `hr`, `all`) — 674 company boards, each verified live (Greenhouse + Lever + Ashby, auto-routed). Just hit Start. Example: preset `ai` → 1,391 jobs (Anthropic 411, Scale AI 191, Databricks 789). ### Three modes - **jobs** — one row per job listing. The classic scraper. - **signals** — per-company hiring intent: N open roles in an area = active investment = a lead. - **techstack** — technographics: which tools a company actually uses, extracted from its own job descriptions with a deterministic dictionary (no LLM, no guessing — every hit is verifiable in the post). Example: Databricks → Spark, AWS, Azure, Python, Scala. Use it to find companies running a stack you sell into. ### What the technographics data actually claims **"Technologies mentioned repeatedly in this company's own job posts"** — not "technologies certainly running in production". Every hit is verifiable: it sits in a public job ad you can open. Single mentions are filtered out by default (`minTechMentions: 2`), because one mention is usually a nice-to-have or a migration target, not the stack. Validated on companies with publicly known stacks: 14/14 expected technologies detected (Databricks→Spark/Scala, GitLab→Ruby/Kubernetes, Vercel→Next.js, Cloudflare→Rust/Go). No implausible claims on the hard cases (MongoDB shows no competing database; Datadog shows no Grafana). ### The money query: competitive displacement `techstack` mode + `usesTech` / `notUsesTech` builds the list a competing vendor pays for: > uses **Datadog**, does **not** use **Grafana** → 18 companies (Coinbase, Chime, Block, Marqeta, Tide, Vercel…) Every tool detected is also a vendor whose competitors want that list. Same for gaps: `categoryGaps` shows categories where a company shows **no** tool at all — an open slot to sell into. Gaps are only reported when enough job text was analysed (`gapsReliable`), so absence means absence, not "we didn't look". ### Input | Field | Meaning | Default | |-------|---------|---------| | `companyPreset` | curated verified boards (use if you don't know slugs) | all | | `companies` | your own ATS board tokens; overrides preset | — | | `locationFilter` | keep jobs whose location contains this (e.g. `Berlin`, `Remote`) | — | | `remoteOnly` | keep only remote-flagged jobs | false | | `departmentFilter` | keep jobs whose department contains this (e.g. `Engineering`) | — | | `maxJobsPerCompany` | cap results per company | 0 (no cap) | | `outputMode` | `jobs` (one row per listing), `signals` (per-company hiring intent), `techstack` (tools a company uses) | jobs | | `source` | `greenhouse` or `lever` | greenhouse | | `roleKeywords` | roles that signal the investment you sell into | sales/AE/SDR/revenue | | `minOpenings` | surge threshold (≥ N matching roles) | 2 | | `sinceDays` | only roles updated within N days | 30 | ### Output (dataset) `company`, `source`, `openingsMatched`, `topDepartment`, `roles[]`, `signalReason`, `boardUrl`. ### Example `roleKeywords: ["machine learning","ml","ai"]` → finds companies scaling AI teams → sell them AI infra / data tools / ML consulting. (Tested live: Stripe 40, Figma 21, GitLab 18 AI/ML roles.) ### Notes - Public endpoints only, rate-limited politely. No personal data (public job posts). - Pay-per-event: charges per signal (`signal` event) once monetization is configured in Console. ### Getting board tokens Greenhouse: the slug in `boards.greenhouse.io/`. Lever: `jobs.lever.co/`. Thousands of companies use these — build target lists per industry. # Actor input Schema ## `companyPreset` (type: `string`): Curated, live-verified boards. 'ai'/'fintech'/... are focused sets; 'all' = the full 674-board market (large runs: use maxJobsPerCompany to cap cost). Ignored when 'companies' is filled. ## `companies` (type: `array`): Your own targets. Greenhouse: the slug in boards.greenhouse.io/. Lever: jobs.lever.co/. Overrides the preset. ## `source` (type: `string`): Which public board API to read (only used for your own 'companies'; presets carry their own source). ## `outputMode` (type: `string`): 'jobs' = one row per job listing (job scraper). 'signals' = per-company hiring-intent aggregate. 'techstack' = which technologies each company uses, extracted from its own job descriptions. ## `roleKeywords` (type: `array`): Leave empty to get ALL jobs. Or filter, e.g. ['machine learning','ml','ai'] for AI roles / AI-investment signals. ## `minOpenings` (type: `integer`): A company is reported only if it has at least this many matching open roles. ## `sinceDays` (type: `integer`): Freshness: count only roles updated in the last N days. 0 = no filter. ## `usesTech` (type: `array`): techstack mode: keep only companies whose detected stack includes ALL of these (e.g. ['Datadog']). ## `notUsesTech` (type: `array`): techstack mode: exclude companies using any of these. Combine with 'usesTech' to build competitive displacement lists (uses Datadog, not Grafana). ## `locationFilter` (type: `string`): jobs mode: keep only jobs whose location contains this text (e.g. 'Berlin', 'Remote', 'United States'). ## `remoteOnly` (type: `boolean`): jobs mode: keep only jobs marked remote or with 'remote' in the location. ## `departmentFilter` (type: `string`): jobs mode: keep only jobs whose department contains this text (e.g. 'Engineering', 'Sales'). ## `maxJobsPerCompany` (type: `integer`): Cap results per company. 0 = no cap (full data). Set it only if you deliberately want a sample: in signals/techstack modes a cap also lowers the reported signal strength and shrinks the text the tech stack is read from. ## `minTechMentions` (type: `integer`): techstack mode: ignore technologies mentioned fewer than N times across the company's posts. A single mention is usually noise (a 'nice to have' or a migration target), not the stack. ## Actor input object example ```json { "companyPreset": "ai", "companies": [], "source": "greenhouse", "outputMode": "jobs", "roleKeywords": [], "minOpenings": 2, "sinceDays": 30, "usesTech": [], "notUsesTech": [], "locationFilter": "", "remoteOnly": false, "departmentFilter": "", "maxJobsPerCompany": 0, "minTechMentions": 2 } ```` # Actor output Schema ## `results` (type: `string`): All items produced by this run. # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("arbogaste/hiring-intent-buying-signals").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 = {} # Run the Actor and wait for it to finish run = client.actor("arbogaste/hiring-intent-buying-signals").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 '{}' | apify call arbogaste/hiring-intent-buying-signals --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=arbogaste/hiring-intent-buying-signals", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ATS Job Scraper — Greenhouse & Lever Boards", "description": "Scrape jobs straight from company ATS boards (Greenhouse, Lever, Ashby) — source-direct, so no LinkedIn/Indeed blocking, no CAPTCHAs, no proxies. 116 verified company boards included. Three modes: job listings, B2B hiring-intent signals, or technographics (which tools a company uses).", "version": "0.1", "x-build-id": "gFehdbwPeRpSFBz7Z" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/arbogaste~hiring-intent-buying-signals/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-arbogaste-hiring-intent-buying-signals", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/arbogaste~hiring-intent-buying-signals/runs": { "post": { "operationId": "runs-sync-arbogaste-hiring-intent-buying-signals", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/arbogaste~hiring-intent-buying-signals/run-sync": { "post": { "operationId": "run-sync-arbogaste-hiring-intent-buying-signals", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "companyPreset": { "title": "Company preset (easiest start)", "enum": [ "none", "ai", "fintech", "devtools", "saas", "marketplace", "health", "hr", "all" ], "type": "string", "description": "Curated, live-verified boards. 'ai'/'fintech'/... are focused sets; 'all' = the full 674-board market (large runs: use maxJobsPerCompany to cap cost). Ignored when 'companies' is filled.", "default": "ai" }, "companies": { "title": "Company board tokens (optional)", "type": "array", "description": "Your own targets. Greenhouse: the slug in boards.greenhouse.io/. Lever: jobs.lever.co/. Overrides the preset.", "default": [], "items": { "type": "string" } }, "source": { "title": "ATS source", "enum": [ "greenhouse", "lever", "ashby" ], "type": "string", "description": "Which public board API to read (only used for your own 'companies'; presets carry their own source).", "default": "greenhouse" }, "outputMode": { "title": "Output mode", "enum": [ "jobs", "signals", "techstack" ], "type": "string", "description": "'jobs' = one row per job listing (job scraper). 'signals' = per-company hiring-intent aggregate. 'techstack' = which technologies each company uses, extracted from its own job descriptions.", "default": "jobs" }, "roleKeywords": { "title": "Role keywords (optional filter)", "type": "array", "description": "Leave empty to get ALL jobs. Or filter, e.g. ['machine learning','ml','ai'] for AI roles / AI-investment signals.", "default": [], "items": { "type": "string" } }, "minOpenings": { "title": "Min matching roles (surge threshold)", "minimum": 1, "type": "integer", "description": "A company is reported only if it has at least this many matching open roles.", "default": 2 }, "sinceDays": { "title": "Only roles updated within N days", "minimum": 0, "type": "integer", "description": "Freshness: count only roles updated in the last N days. 0 = no filter.", "default": 30 }, "usesTech": { "title": "Uses these technologies", "type": "array", "description": "techstack mode: keep only companies whose detected stack includes ALL of these (e.g. ['Datadog']).", "default": [], "items": { "type": "string" } }, "notUsesTech": { "title": "Does NOT use these", "type": "array", "description": "techstack mode: exclude companies using any of these. Combine with 'usesTech' to build competitive displacement lists (uses Datadog, not Grafana).", "default": [], "items": { "type": "string" } }, "locationFilter": { "title": "Location contains", "type": "string", "description": "jobs mode: keep only jobs whose location contains this text (e.g. 'Berlin', 'Remote', 'United States').", "default": "" }, "remoteOnly": { "title": "Remote only", "type": "boolean", "description": "jobs mode: keep only jobs marked remote or with 'remote' in the location.", "default": false }, "departmentFilter": { "title": "Department contains", "type": "string", "description": "jobs mode: keep only jobs whose department contains this text (e.g. 'Engineering', 'Sales').", "default": "" }, "maxJobsPerCompany": { "title": "Max jobs per company", "minimum": 0, "type": "integer", "description": "Cap results per company. 0 = no cap (full data). Set it only if you deliberately want a sample: in signals/techstack modes a cap also lowers the reported signal strength and shrinks the text the tech stack is read from.", "default": 0 }, "minTechMentions": { "title": "Min mentions per technology", "minimum": 1, "type": "integer", "description": "techstack mode: ignore technologies mentioned fewer than N times across the company's posts. A single mention is usually noise (a 'nice to have' or a migration target), not the stack.", "default": 2 } } }, "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 } } } } } } } } } } ```