Hiring Intelligence Monitor — Turn Job Postings Into Buying Signals

Get notified the moment a target account starts hiring for Kubernetes, a data platform, security, or an entire new team — weeks before they issue an RFP. When a company hires for a technology, they are usually about to buy that technology. This Actor turns public job boards into a structured pipeline of account-level buying signals for B2B sales and growth teams.

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What problem does this solve?

Your SDRs and AEs already track funding rounds and product launches. But the earliest, most honest buying signal is who a company is hiring:

• A target posts 6 infrastructure roles in two weeks → they are scaling an org and a budget is being allocated.
• A prospect starts recruiting Kubernetes / cloud engineers → they are adopting (or consolidating) cloud-native tooling — a fit for infra, observability, and DevOps vendors.
• A customer suddenly hires security & compliance → expansion / cross-sell window for security products.

Today these signals are either invisible or locked behind enterprise people-analytics platforms such as Revelio Labs ($85K+/year). This Actor is a $29–99/month lightweight alternative: you give it a list of target companies, and it watches their public applicant tracking systems (Greenhouse, Lever, Ashby …), computes the signals, and sends them straight to Slack.

No browser, no proxies, no CAPTCHAs — it uses each ATS's public structured JSON API, so runs are fast, cheap, and low-maintenance.

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Example use cases

• Outbound prospecting — prioritize accounts that are actively hiring for your category. A DevOps platform vendor watches for Kubernetes/SRE hiring; a data vendor watches for data/ML roles.
• Expansion & renewal — detect when existing customers open new teams or offices, and route the insight to the account owner.
• Competitive & market intel — see which companies (or competitors) are scaling particular functions.
• Investment / partnerships — lightweight headcount-growth tracking without a six-figure data contract.

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Inputs

Configure a list of target companies, each with a name and either an ATS type + identifier or a careers URL (the Actor auto-detects the ATS):

{
  "companies": [
    { "name": "Stripe",  "ats_type": "greenhouse", "ats_id": "stripe" },
    { "name": "Palantir", "ats_type": "lever", "ats_id": "palantir" },
    { "name": "Linear", "ats_type": "ashby", "ats_id": "linear" },
    { "name": "Acme Corp", "url": "https://acme.com/careers" }
  ],
  "surgeMinCount": 5,
  "surgeGrowthPct": 0.5,
  "notify": {
    "slack_webhook_url": "https://hooks.slack.com/services/T000/B000/xxx"
  }
}

You can also paste a raw URL that returns job postings as JSON, or a normal careers page (the Actor falls back to HTML text extraction). All fields are documented in the Actor input form.

Supported sources

Source	How to specify	Structured fields
Greenhouse	ats_type: greenhouse, ats_id: <board token>	team/department, location, office, remote, posted date, full description
Lever	ats_type: lever, ats_id: <company>	team, category, location, commitment, remote, posted date, plain-text description
Ashby	ats_type: ashby, ats_id: <org>	team/department, location, employment type, remote, posted date, plain-text description
Generic JSON	ats_type: generic_json, url: <JSON endpoint>	auto-mapped from common field names
Any careers page	url: <page URL> (auto-detect → HTML fallback)	extracted posting text

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Outputs

Every run produces a structured JSON report pushed to the dataset (and saved to the key-value store), plus an optional Slack/email/webhook alert.

Signal types

Signal	Meaning
job.opened	A new role appeared (stable job ID)
job.closed	A role was removed
job.changed	Team, location, employment type, or description changed
team.growth / team.contraction	Net roles added/removed per team
team.surge	A team added ≥ surgeMinCount roles or grew ≥ surgeGrowthPct in one run — a hiring surge
tech.stack_signal	New roles reference a technology theme — a buying-intent signal
company.net_hiring	Company-wide net headcount change and total open roles

Each signal includes type, strength (low/medium/high), summary, related jobs with direct links, timestamps, and matched keywords.

Example signal object

{
  "company": "Demo Target Inc",
  "ats": "greenhouse",
  "signal_type": "team.surge",
  "strength": "high",
  "summary": "HIRING SURGE in Engineering: 6 new roles (200% growth)",
  "team": "Engineering",
  "opened_count": 6,
  "closed_count": 0,
  "net_change": 6,
  "growth_pct": 2.0,
  "related_jobs": [
    {"job_id": "n01", "title": "Senior Kubernetes Engineer",
     "location": "Remote, US", "url": "https://boards.example.com/jobs/n01"}
  ],
  "detected_at": "2026-09-25T17:05:00Z"
}

Example Slack message (plain-text digest)

Hiring Intelligence Report
Run at: 2026-09-25T17:05:00Z UTC
Companies checked: 1 | Signals: 17
================================================
[JOB.OPENED] Demo Target Inc: New role opened: Senior Kubernetes Engineer
    https://boards.example.com/jobs/n01
[JOB.CLOSED] Demo Target Inc: Role removed: Account Executive
    https://boards.example.com/jobs/j01
[JOB.CHANGED] Demo Target Inc: Role updated (location, is_remote): Backend Engineer
    https://boards.example.com/jobs/j02
[TEAM.SURGE] Demo Target Inc: HIRING SURGE in Engineering: 6 new roles (200% growth)
[TECH.STACK_SIGNAL] Demo Target Inc: Buying signal: 1 new role(s) reference Kubernetes / Container Orchestration - potential buyer of Container / Kubernetes platforms
    Keywords: Helm, Istio, Kubernetes
[COMPANY.NET_HIRING] Demo Target Inc overall: added 5 open role(s) (15 currently open)

A full rendered example ships in examples/e2e_signal_message.txt.

Technology-theme dictionary

Buying-intent tags are matched against an editable keyword dictionary shipped at config/keywords.json. Default themes:

cloud · kubernetes · data · ml_ai · devops · security · salesforce_crm · observability · erp_finance · mobile · sales_growth

You can extend it (add products, categories, or your own keywords) directly from the Actor input (customKeywords) or by editing the config file.

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Scheduling

Set it and forget it. Run the Actor on a schedule (daily or weekly recommended) from the Apify Console — Schedules → Create schedule. Baselines are stored in the key-value store, so each run compares against the previous one. The first run for any company only builds a baseline and never alerts (so you don't get flooded with hundreds of "new" roles).

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Pricing & free usage

This Actor runs on the Apify Pay-Per-Event (PPE) monetization model. The events below are registered in the Apify Console → Monetization and triggered from the code:

Event key	Price	Charged when
company-monitored	$0.02	each target company's job board is successfully fetched
signal-detected (primary)	$0.05	each actionable signal is produced (new/closed role, team surge, tech buying signal …)

Platform compute usage is billed separately on top of the events. Event keys may only contain letters, digits, _ and - (no dots), which is why they use hyphens.

With ~50 target companies and a typical 10–20 signals per daily run, a month of daily monitoring lands comfortably in the tens of dollars range — versus $85,000+/year for enterprise people-analytics suites. You only pay for the accounts and signals you actually use. The first run for every company only builds a baseline (no signal-detected charges), so onboarding a large target list stays cheap.

The code triggers these events at clearly marked points using the official Actor.charge() and push_data(..., charged_event_name=...) SDK methods.

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How to use (step by step)

1. Open the Actor on the Apify Store (or run it privately).
2. In the input form, add your target companies — paste a Greenhouse/Lever/Ashby token or just a careers-page URL.
3. Adjust the surge threshold and tech keywords (defaults work out of the box).
4. Add a Slack incoming webhook (Apps → Incoming Webhooks in Slack) to receive alerts; leave blank to read results from the dataset.
5. Click Start for the first run (baseline only).
6. Go to Schedules, create a daily/weekly schedule, and attach this Actor.
7. Watch buying-intent signals arrive in Slack.

Notifications: Slack block-kit messages, generic JSON webhooks (Feishu/Lark and DingTalk-compatible), and SMTP email are all supported.

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Notes & limitations

• Only publicly available job-board data is read; no login, scraping of private data, or bypass of access controls.
• Signal quality depends on how a company structures its postings; some boards expose richer fields than others.
• For companies on niche ATSs, use the custom-JSON or HTML-fallback source.
• Respect each platform's terms of service and rate limits; the Actor already spaces requests and identifies itself with a clear User-Agent.

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Project structure

hiring-intel-actor/
├── .actor/
│   ├── actor.json
│   └── input_schema.json
├── src/
│   ├── main.py          # Actor entrypoint (Apify + local mode)
│   ├── engine.py        # orchestration: fetch -> diff -> signals -> notify
│   ├── adapters.py      # Greenhouse / Lever / Ashby / custom JSON / HTML
│   ├── diff_engine.py   # opened/closed/changed, surge, tech signals
│   ├── keywords.py      # theme keyword matcher
│   ├── storage.py       # local SQLite <-> Apify key-value store
│   ├── notifiers.py     # Slack / generic webhook / SMTP
│   └── http_client.py   # resilient HTTP layer
├── config/keywords.json
├── examples/
├── tests/
├── Dockerfile
└── requirements.txt