LinkedIn Job Signals Automation: Apify + OpenAI + Notion

Turn new LinkedIn job posts into actionable hiring signals—automatically

This n8n workflow runs every Monday to scrape recent LinkedIn job listings with Apify, uses OpenAI to extract inferred hiring-based “buying signals,” and saves each result as a new entry in Notion—complete with a relevance score and the original job link.

What this workflow does

• Schedules execution every Monday at 08:00.
• Targets specific job searches by defining job queries, locations, what you sell, and a max number of posts to analyze.
• Collects job posts from the past week using Apify’s LinkedIn Jobs Scraper.
• Filters for analyzable content: it skips posts missing a company and posts that don’t include enough text (title or description).
• Avoids duplicates by checking whether a job was processed previously using the job ID or URL.
• Analyzes each remaining post with OpenAI to generate JSON that describes:
  • what the company is building
  • the implied need
  • the inferred timing
  • a 1–5 relevance score
• Creates Notion pages in your database storing the company, role, inferred signal fields, relevance, and the job link.

Use cases

• SaaS operators monitoring hiring trends to spot teams likely evaluating your product.
• Automation engineers building a repeatable LinkedIn job signals pipeline in n8n.
• Go-to-market teams turning “what companies are building” into prioritization lists inside Notion.

Technical details

• Integrations/Nodes: schedule trigger, set, filter, sticky note, apifyn8n-nodes-apify (Apify), and Notion.
• Credentials required: Apify API token, OpenAI API key, and Notion access.
• Notion setup: configure your Notion database ID and ensure matching properties (Role, Building, Implied need, Timing, Relevance, Link).
• Configuration: update search queries, locations, “what you sell,” and maxItems to match your ideal buying signals.