# 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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/<token>`. Lever: `jobs.lever.co/<token>`.
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/<token>. Lever: jobs.lever.co/<token>. 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 '<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("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 '<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("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 <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/Fo5WWJGs3ODhuJHle/builds/gFehdbwPeRpSFBz7Z/openapi.json