# ATS Job Scraper: Greenhouse, Ashby & Lever + Hiring Intel (`acebuilds/ats-jobs-intel`) Actor Scrape fresh jobs from company ATS boards (Greenhouse, Ashby, Lever) before they hit Indeed, plus hiring intelligence: velocity, teams & buying signals. - **URL**: https://apify.com/acebuilds/ats-jobs-intel.md - **Developed by:** [Ace](https://apify.com/acebuilds) (community) - **Categories:** Jobs, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 job scrapeds 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.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 ## ATS Jobs Intelligence **Fresh jobs straight from company career boards — before they hit Indeed — plus a hiring-intelligence summary for every company.** Most job scrapers fight Indeed's and LinkedIn's anti-bot defenses and hand you stale, deduplicated rows. This actor goes to the **source**: the public JSON APIs behind companies' own Applicant Tracking Systems (Greenhouse, Ashby, Lever). The data is fresh, structured, and un-scraped — and on top of it you get a per-company **intelligence layer** that answers the question raw rows can't: *is this company scaling, and where?* Built for **recruiters** (fresh roles first), **sales/BD teams** (hiring is a buying signal), and **competitive intel** (watch a rival's org grow in real time). --- ### Supported ATS providers | Provider | Board spec | Where the slug comes from | |---|---|---| | Greenhouse | `greenhouse:slug` | `boards.greenhouse.io/` | | Ashby | `ashby:slug` | `jobs.ashbyhq.com/` | | Lever | `lever:slug` | `jobs.lever.co/` | All three are public, unauthenticated endpoints (verified live). No API keys required. --- ### Input ```json { "boards": ["greenhouse:airbnb", "ashby:ramp", "lever:veeva"], "keywords": ["engineer", "product"], "department": "Engineering", "location": "Remote", "remoteOnly": false, "postedWithinDays": 30, "maxJobsPerCompany": 1000, "includeIntelligence": true } ```` | Field | Type | Notes | |---|---|---| | `boards` (required) | string\[] | `"provider:slug"` entries | | `keywords` | string\[] | Title must contain at least one (case-insensitive) | | `department` | string | Department contains (case-insensitive) | | `location` | string | Location contains (case-insensitive) | | `remoteOnly` | boolean | Only remote-flagged roles | | `postedWithinDays` | integer | Only roles published within N days (0 = off) | | `maxJobsPerCompany` | integer | Cap on emitted job rows per board | | `includeIntelligence` | boolean | Emit the per-company summary row (default true) | | `mode` | string | `snapshot` (default) or `watchlist` — see below | | `watchlistName` | string | Names the baseline in watchlist mode (default `default`) | *** ### Output The dataset contains two record types, distinguished by `recordType` (with matching dataset **views**): **`recordType: "job"`** — one per matched role: ``` provider, company, id, title, department, team, location, remote, employmentType, publishedAt, updatedAt, url, applyUrl ``` **`recordType: "company_intelligence"`** — one per board (unless disabled): ``` totalOpenRoles, newRoles7d, newRoles30d, hiringVelocity, pctOpenedLast30d, remotePct, topDepartments, topLocations, fastestGrowingDepartment, seniorityMix, buyingSignals, summary ``` `hiringVelocity` is one of `Aggressive` (≥25% of roles opened in 30d), `Active` (≥10%), `Steady` (<10%), `Frozen` (0 new), or `Unknown` (no dated roles). Intelligence is computed on the company's **full** role set, so it reflects real hiring posture regardless of your keyword/location filter. Example `summary`: > Ramp: 125 open roles, hiring aggressive · 41 opened in the last 30d · fastest-growing team: Sales · 38% remote. Signals: Scaling Sales — 6 new roles in 30d; Building leadership — 2 new director/exec role(s) in 30d. *** ### Watchlist mode — track changes over time Set `mode: "watchlist"` to turn the actor from a one-time snapshot into a **change feed**. Instead of returning every current job each run, it remembers the last run and reports only **what changed** since — ideal for a **scheduled** run (daily/weekly) that alerts you when target companies start (or stop) hiring. **How it works:** 1. **First run** on a given `watchlistName` + board just saves a **baseline** (a free `baseline` row — nothing is charged). 2. **Every run after** diffs the live board against that baseline and emits `recordType: "change"` rows: | `changeType` | Meaning | Billed? | |---|---|---| | `new_role` | A role that wasn't there last run | ✅ as `job-scraped` | | `removed_role` | A role that's been filled or pulled | Free | | `company_shift` | Posture digest: velocity change, new buying signals, new/removed counts | ✅ as `company-intelligence` | | `no_change` | Nothing changed for that board this run | Free | | `baseline` | First-run seed | Free | 3. The baseline advances to the current state after each run, so you only ever see genuinely new changes. Use `watchlistName` to keep separate histories (e.g. `competitors`, `target-accounts`). Set `includeIntelligence: false` to get only role-level changes without the posture digest. See the **Changes** dataset view for a clean table. **Who it's for:** sales/BD teams treating hiring as a buying signal, recruiters watching competitors open roles, and investors/analysts tracking a company's org growth week over week. > Tip: schedule watchlist runs in Apify (Schedules) and wire the dataset to a webhook/Slack for automatic hiring alerts. ### Pricing **Pay per event** — you are charged only for the results you receive: - **`job-scraped`** — one charge per job row returned. - **`company-intelligence`** — one charge per company hiring-intelligence summary. Boards that error (wrong slug, company migrated off that ATS) are **never charged** — error rows are free. The intelligence summary is priced separately from job rows because it's the differentiator versus commodity single-board scrapers: set `includeIntelligence: false` to skip it and pay for job rows only. *** ### Notes & limits - **Provider field coverage differs** (from each ATS's public API): Greenhouse omits `team`/`employmentType`; Ashby and Lever omit `updatedAt`. Missing fields are `null`, never guessed. - Lever's `createdAt` is epoch-ms and is normalized to ISO-8601 like the others. - A board that errors (wrong slug, company migrated off that ATS) emits a `recordType: "error"` row and does not stop the run. - Only `isListed` Ashby roles are returned. # Actor input Schema ## `boards` (type: `array`): List of ATS boards to scrape, each as "provider:slug". Provider is one of greenhouse, ashby, lever. The slug is the company's board identifier in its careers URL (e.g. boards.greenhouse.io/AIRBNB → "greenhouse:airbnb", jobs.ashbyhq.com/RAMP → "ashby:ramp", jobs.lever.co/VEEVA → "lever:veeva"). ## `keywords` (type: `array`): Only return jobs whose title contains at least one of these keywords (case-insensitive). Leave empty to return all roles. ## `department` (type: `string`): Only return jobs whose department contains this text (case-insensitive). E.g. "Engineering", "Sales". ## `location` (type: `string`): Only return jobs whose location contains this text (case-insensitive). E.g. "New York", "London", "Remote". ## `remoteOnly` (type: `boolean`): If true, return only roles flagged remote (or whose location mentions remote). ## `postedWithinDays` (type: `integer`): Only return roles first published within this many days. 0 (default) disables the filter. Note: roles without a publish date are excluded when this is set. ## `maxJobsPerCompany` (type: `integer`): Cap on job rows emitted per board. Lower = faster + cheaper. ## `includeIntelligence` (type: `boolean`): If true (default), emit one company-intelligence summary row per board: hiring velocity, department investment, seniority mix, and buying signals. ## `mode` (type: `string`): snapshot (default) = return all current jobs + intelligence every run. watchlist = remember the last run and report only what CHANGED since (new roles, removed/filled roles, hiring-posture shifts). Watchlist is built for scheduled runs — the first run just saves a baseline. New roles bill as job-scraped and posture-shift digests as company-intelligence; removed roles and the baseline are free. ## `watchlistName` (type: `string`): Names the baseline this run diffs against, so you can keep multiple independent watchlists (e.g. "competitors", "target-accounts"). Runs sharing a name and board build on the same history. Ignored in snapshot mode. ## Actor input object example ```json { "boards": [ "greenhouse:airbnb", "ashby:ramp", "lever:veeva" ], "keywords": [], "department": "", "location": "", "remoteOnly": false, "postedWithinDays": 0, "maxJobsPerCompany": 1000, "includeIntelligence": true, "mode": "snapshot", "watchlistName": "default" } ``` # 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 = { "boards": [ "greenhouse:airbnb", "ashby:ramp", "lever:veeva" ], "keywords": [] }; // Run the Actor and wait for it to finish const run = await client.actor("acebuilds/ats-jobs-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 = { "boards": [ "greenhouse:airbnb", "ashby:ramp", "lever:veeva", ], "keywords": [], } # Run the Actor and wait for it to finish run = client.actor("acebuilds/ats-jobs-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 '{ "boards": [ "greenhouse:airbnb", "ashby:ramp", "lever:veeva" ], "keywords": [] }' | apify call acebuilds/ats-jobs-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=acebuilds/ats-jobs-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ATS Job Scraper: Greenhouse, Ashby & Lever + Hiring Intel", "description": "Scrape fresh jobs from company ATS boards (Greenhouse, Ashby, Lever) before they hit Indeed, plus hiring intelligence: velocity, teams & buying signals.", "version": "0.1", "x-build-id": "n0Dz0vQab05dHT5zf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/acebuilds~ats-jobs-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-acebuilds-ats-jobs-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/acebuilds~ats-jobs-intel/runs": { "post": { "operationId": "runs-sync-acebuilds-ats-jobs-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/acebuilds~ats-jobs-intel/run-sync": { "post": { "operationId": "run-sync-acebuilds-ats-jobs-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": [ "boards" ], "properties": { "boards": { "title": "Company Boards (provider:slug)", "type": "array", "description": "List of ATS boards to scrape, each as \"provider:slug\". Provider is one of greenhouse, ashby, lever. The slug is the company's board identifier in its careers URL (e.g. boards.greenhouse.io/AIRBNB → \"greenhouse:airbnb\", jobs.ashbyhq.com/RAMP → \"ashby:ramp\", jobs.lever.co/VEEVA → \"lever:veeva\").", "items": { "type": "string" } }, "keywords": { "title": "Title Keywords (optional)", "type": "array", "description": "Only return jobs whose title contains at least one of these keywords (case-insensitive). Leave empty to return all roles.", "items": { "type": "string" } }, "department": { "title": "Department contains (optional)", "type": "string", "description": "Only return jobs whose department contains this text (case-insensitive). E.g. \"Engineering\", \"Sales\".", "default": "" }, "location": { "title": "Location contains (optional)", "type": "string", "description": "Only return jobs whose location contains this text (case-insensitive). E.g. \"New York\", \"London\", \"Remote\".", "default": "" }, "remoteOnly": { "title": "Remote roles only", "type": "boolean", "description": "If true, return only roles flagged remote (or whose location mentions remote).", "default": false }, "postedWithinDays": { "title": "Posted within N days (optional)", "minimum": 0, "maximum": 365, "type": "integer", "description": "Only return roles first published within this many days. 0 (default) disables the filter. Note: roles without a publish date are excluded when this is set.", "default": 0 }, "maxJobsPerCompany": { "title": "Max jobs per company", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Cap on job rows emitted per board. Lower = faster + cheaper.", "default": 1000 }, "includeIntelligence": { "title": "Include hiring-intelligence summary", "type": "boolean", "description": "If true (default), emit one company-intelligence summary row per board: hiring velocity, department investment, seniority mix, and buying signals.", "default": true }, "mode": { "title": "Mode", "enum": [ "snapshot", "watchlist" ], "type": "string", "description": "snapshot (default) = return all current jobs + intelligence every run. watchlist = remember the last run and report only what CHANGED since (new roles, removed/filled roles, hiring-posture shifts). Watchlist is built for scheduled runs — the first run just saves a baseline. New roles bill as job-scraped and posture-shift digests as company-intelligence; removed roles and the baseline are free.", "default": "snapshot" }, "watchlistName": { "title": "Watchlist name (watchlist mode only)", "type": "string", "description": "Names the baseline this run diffs against, so you can keep multiple independent watchlists (e.g. \"competitors\", \"target-accounts\"). Runs sharing a name and board build on the same history. Ignored in snapshot mode.", "default": "default" } } }, "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 } } } } } } } } } } ```