# Hiring Leads Finder — Company Contacts & Growth Signals (`qualifyops/hiring-leads-finder-company-contacts`) Actor Turn LinkedIn job-search results into one deduplicated record per company: roles hiring, a deterministic hiring signal, the likely commercial event, public emails/phones/contact page, and a transparent opportunity score with evidence. - **URL**: https://apify.com/qualifyops/hiring-leads-finder-company-contacts.md - **Developed by:** [QualifyOps](https://apify.com/qualifyops) (community) - **Categories:** Lead generation, Jobs, Automation - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 results 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 ### Try it in 60 seconds **You give:** a public LinkedIn job-search URL (a role + location). **You get:** one scored record per **company that is hiring** — roles, a deterministic hiring signal, the likely commercial reason, public emails/phones, contact page, and the evidence job URLs. **Price:** **$0.01 per company** (intro). A 100-company run costs about **$1**. **Run it now:** press **Start** above and paste a job-search URL — results stream to the dataset (export JSON/CSV/Excel). **Real sample — live run, 8 companies, total cost $0.0099:** - Albury Wodonga Health — signal `general_hiring`, `awh.foundation@awh.org.au`, +61 2 6051 7111 - Cairns Family & Cosmetic Dental — +61 7 4036 4391 - Ramsay Health Care — +61 2 9433 3444 Deduped to one row per company, junk-filtered (no copyright-date phones, no widget emails). **For agents & automation:** call it via the Apify API/MCP — one run returns deduped, CRM-ready company records with contacts + signal. Schedule it for a recurring hiring-signal feed. --- ## Hiring Leads Finder — Company Contacts & Growth Signals Turn fresh **LinkedIn job-search results** into **one deduplicated record per company**: which roles they're hiring, what the hiring most likely means commercially, public contact routes, and a transparent opportunity score — with evidence. The dominant LinkedIn Jobs scraper already gives you *raw job rows*. This Actor completes the chain those rows leave to you: > raw job rows → **unique companies actively hiring → what the hiring implies → public contact routes → evidence → transparent score** --- ### Why this instead of a raw job scraper A LinkedIn/Indeed job scraper returns one row **per posting** — 30 postings from the same company, no contacts, and no way to tell what the hiring *means*. You still have to collapse rows to companies, find contacts, and decide who to reach first. | | Raw job scraper | Generic "hiring lead" actors | **Hiring Leads Finder** | |---|---|---|---| | Output unit | one row per **job** | one row per lead | one row per **company** (deduplicated) | | Hiring signal | none | opaque | **deterministic, auditable classes** | | Score | none | black-box | **transparent — every point explained** | | Public contacts | none | sometimes | **emails + phones + contact page, one pass** | | Evidence | — | — | **source job URLs per company** | Pair it with any LinkedIn jobs scraper (it calls a proven one for you) and it returns the part you actually wanted: **which companies are hiring, what it implies, and how to reach them.** --- ### What it does 1. Calls the proven upstream **LinkedIn Jobs Scraper** (`curious_coder/linkedin-jobs-scraper`) to fetch fresh public job rows for your search URL(s). 2. Normalizes company identity (website domain → LinkedIn URL → cleaned name). 3. Groups job rows into **one record per company** and collapses duplicate rows. 4. Determines the latest posting date and the number of matching roles. 5. Applies optional **role-keyword** and **freshness** filters. 6. Classifies the **hiring signal** with deterministic, auditable rules. 7. Scans the public company website for **emails, phones, a contact page and social links**. 8. Calculates a **transparent opportunity score** (every point is explained). 9. Returns one dataset row per company, sorted by score. No CRM, no historical monitoring, no AI-written outreach, no opaque score. --- ### Input | Field | Type | Notes | |-------|------|-------| | `jobSearchUrls` *(required)* | array | One or more public LinkedIn job-search URLs. | | `maxJobs` | integer | Upper bound on raw rows from upstream. | | `roleKeywords` | array | Keep only titles containing one of these (case-insensitive). | | `freshnessDays` | integer | Keep only jobs posted within N days. Unparseable dates are kept and flagged. | | `includeWebsiteContacts` | boolean | Default `true`. Scan company websites for public contacts. | | `maxCompanies` | integer | Return only the top N by score. | | `requestTimeoutSecs`, `maxPagesPerSite`, `maxConcurrency` | integer | Website-scan tuning. | See [`sample_input.json`](./sample_input.json). --- ### Output (one row per company) `company_name`, `company_website`, `company_linkedin`, `company_domain`, `new_jobs_count`, `roles_hiring`, `latest_job_date`, `hiring_signal`, `commercial_reason`, `classification_reasons`, `matched_titles`, `public_emails`, `public_phones`, `contact_page`, `social_links`, `signal_score`, `score_reasons`, `evidence_job_urls`, `upstream_actor`, `upstream_run_id`, `status`, `error`, `processed_at`. See [`sample_output.json`](./sample_output.json). #### Hiring-signal classes `sales_expansion`, `marketing_expansion`, `technical_product_investment`, `operations_expansion`, `new_location_or_service_capacity`, `clinical_practitioner_expansion`, `general_hiring`. The dominant class is the one matching the most distinct job titles; ties break by a fixed priority order. Every record carries the matched titles and the reasons, so the classification is auditable. --- ### Opportunity score (transparent weights) The score measures **actionability**, not generic company quality. It is the sum of the fired components below, capped at **100**: | Component | Weight | Fires when | |-----------|-------:|------------| | Multiple related vacancies | +15 | 2 or more current roles | | High vacancy volume | +10 | 4 or more current roles | | Senior / expansion-linked role | +15 | a senior/leadership title is present | | Recent posting | +15 | latest matching role within the freshness window (default 7 days) | | Company website resolved | +10 | a website was found | | Public email found | +20 | at least one public email | | Public phone found | +10 | at least one public phone | | Dedicated contact page | +10 | a contact page was found | | Clear signal classification | +10 | signal is not `general_hiring` | | Multiple evidence URLs | +5 | 2 or more source job URLs | Each fired component appears verbatim in `score_reasons` (e.g. `+20 Public email found`). --- ### Example uses **General B2B.** Australian companies hiring several sales, marketing, operations or technical roles in the last seven days → contactable company opportunities. **Aesthetic clinics.** Filter `roleKeywords` for `cosmetic nurse`, `dermal therapist`, `laser therapist`, `clinic manager`, `women's health` to surface clinics showing likely service-capacity or location expansion. --- ### Limitations & compliance boundary - **Public data only.** Contacts come from publicly published company web pages and the upstream's public LinkedIn job data. No email verification, no private-data access, no gated profiles. - **Hiring is a signal, not proven purchase intent.** The commercial reason is an interpretation, presented as a hypothesis to prioritise outreach — not a guarantee. - Some public pages embed third-party widget emails (e.g. support vendors); these can appear among `public_emails`. Review before outreach. - Posting dates from the source vary in format; unparseable dates are kept and flagged (`status: partial`) rather than guessed. - Respect LinkedIn's and each website's terms; use results for legitimate B2B outreach consistent with applicable law (e.g. the Australian Spam Act — include identification and an unsubscribe path in any email). --- ### Pricing (recommendation — not validated) > Working hypothesis: **$0.02 per qualified company opportunity**, or > **$20 per 1,000 output companies**. This is an unvalidated starting point, stated separately from technical completion. It should be confirmed by real transactions, not by this document. --- ### Related actor Working from a list of company **websites** instead of job searches? See **Website Lead Qualifier & Contact Finder** (same author) — it turns any list of company sites into clean, scored lead records with public emails, phones and socials: https://apify.com/qualifyops/website-lead-qualifier-contact-finder --- ### Upstream dependency See [`CONTRACT.md`](./CONTRACT.md). Requires `APIFY_TOKEN` at runtime so the platform can call the upstream Actor. The upstream call uses `scrapeCompany: true` to resolve company websites for contact extraction. --- ### Development ```bash python -m venv .venv && . .venv/bin/activate pip install beautifulsoup4 httpx pytest python -m pytest -q # unit + fixture tests python scripts/dry_run.py # offline end-to-end on the bundled fixture ## Live end-to-end against the Apify platform (needs a token): export APIFY_TOKEN=apify_api_xxx pip install apify-client python scripts/live_run.py --url "" --max-jobs 40 --freshness-days 14 ```` # Actor input Schema ## `jobSearchUrls` (type: `array`): One or more public LinkedIn job-search result URLs (e.g. a filtered search for a role and location). ## `maxJobs` (type: `integer`): Upper bound on raw job rows requested from the upstream scraper. Leave empty to use the upstream default. ## `roleKeywords` (type: `array`): Only keep jobs whose title contains one of these keywords (case-insensitive). Leave empty to keep all roles. ## `freshnessDays` (type: `integer`): Only keep jobs posted within this many days. Jobs with an unparseable date are kept and flagged. Leave empty to keep all. ## `includeWebsiteContacts` (type: `boolean`): Scan each resolved company website for public emails, phones, a contact page and social links. ## `maxCompanies` (type: `integer`): Return only the top N companies by opportunity score. Leave empty to return all. ## `requestTimeoutSecs` (type: `integer`): How long to wait for each company web page before giving up. ## `maxPagesPerSite` (type: `integer`): Homepage plus likely contact/about/team pages scanned per company website. ## `maxConcurrency` (type: `integer`): Number of company websites scanned in parallel. Lower if sites are slow or fragile. ## Actor input object example ```json { "jobSearchUrls": [ { "url": "https://www.linkedin.com/jobs/search/?keywords=cosmetic%20nurse&location=Australia" } ], "roleKeywords": [], "includeWebsiteContacts": true, "requestTimeoutSecs": 20, "maxPagesPerSite": 4, "maxConcurrency": 5 } ``` # Actor output Schema ## `companies` (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 = { "jobSearchUrls": [ { "url": "https://www.linkedin.com/jobs/search/?keywords=cosmetic%20nurse&location=Australia" } ], "roleKeywords": [] }; // Run the Actor and wait for it to finish const run = await client.actor("qualifyops/hiring-leads-finder-company-contacts").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 = { "jobSearchUrls": [{ "url": "https://www.linkedin.com/jobs/search/?keywords=cosmetic%20nurse&location=Australia" }], "roleKeywords": [], } # Run the Actor and wait for it to finish run = client.actor("qualifyops/hiring-leads-finder-company-contacts").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 '{ "jobSearchUrls": [ { "url": "https://www.linkedin.com/jobs/search/?keywords=cosmetic%20nurse&location=Australia" } ], "roleKeywords": [] }' | apify call qualifyops/hiring-leads-finder-company-contacts --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=qualifyops/hiring-leads-finder-company-contacts", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hiring Leads Finder — Company Contacts & Growth Signals", "description": "Turn LinkedIn job-search results into one deduplicated record per company: roles hiring, a deterministic hiring signal, the likely commercial event, public emails/phones/contact page, and a transparent opportunity score with evidence.", "version": "0.1", "x-build-id": "QrFTDPpd7bFjmGsOG" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/qualifyops~hiring-leads-finder-company-contacts/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-qualifyops-hiring-leads-finder-company-contacts", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/qualifyops~hiring-leads-finder-company-contacts/runs": { "post": { "operationId": "runs-sync-qualifyops-hiring-leads-finder-company-contacts", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/qualifyops~hiring-leads-finder-company-contacts/run-sync": { "post": { "operationId": "run-sync-qualifyops-hiring-leads-finder-company-contacts", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "jobSearchUrls" ], "properties": { "jobSearchUrls": { "title": "LinkedIn job-search URLs", "type": "array", "description": "One or more public LinkedIn job-search result URLs (e.g. a filtered search for a role and location).", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxJobs": { "title": "Maximum jobs to fetch", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Upper bound on raw job rows requested from the upstream scraper. Leave empty to use the upstream default." }, "roleKeywords": { "title": "Role keywords (optional filter)", "type": "array", "description": "Only keep jobs whose title contains one of these keywords (case-insensitive). Leave empty to keep all roles.", "items": { "type": "string" } }, "freshnessDays": { "title": "Freshness window (days)", "minimum": 1, "maximum": 90, "type": "integer", "description": "Only keep jobs posted within this many days. Jobs with an unparseable date are kept and flagged. Leave empty to keep all." }, "includeWebsiteContacts": { "title": "Find public website contacts", "type": "boolean", "description": "Scan each resolved company website for public emails, phones, a contact page and social links.", "default": true }, "maxCompanies": { "title": "Maximum companies to return", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Return only the top N companies by opportunity score. Leave empty to return all." }, "requestTimeoutSecs": { "title": "Website request timeout (seconds)", "minimum": 5, "maximum": 60, "type": "integer", "description": "How long to wait for each company web page before giving up.", "default": 20 }, "maxPagesPerSite": { "title": "Max pages scanned per website", "minimum": 1, "maximum": 8, "type": "integer", "description": "Homepage plus likely contact/about/team pages scanned per company website.", "default": 4 }, "maxConcurrency": { "title": "Websites scanned at once", "minimum": 1, "maximum": 10, "type": "integer", "description": "Number of company websites scanned in parallel. Lower if sites are slow or fragile.", "default": 5 } } }, "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 } } } } } } } } } } ```