I built a job-search intelligence pipeline combining Selenium scraping, data normalization, ATS pattern analysis and n8n automation. The bot collected roughly 10,000 LinkedIn jobs in about 3 hours, extracting role, company, location, seniority, tech stack and description. I then compared each offer against my CV to identify where I could be one of the strongest-fit candidates. With that shortlist, I sent only 15 emails and got 10 interviews, including 5 processes whose official deadline had already closed.
Problem, stack and result
Problem solved
Automation system that collected roughly 10,000 LinkedIn job offers in 3 hours, analyzed ATS patterns and helped me get 10 interviews from only 15 emails.
Technologies used
Core stack: Python, Selenium, n8n. Project technologies: Python, Selenium, n8n, LinkedIn, ATS Analysis, LLMs, CV Matching, Data Cleaning.
What I did
My role was Author · Automation & AI Workflow. I worked on the technical implementation, product approach and enough documentation for the result to be explained and evolved.
Result or learning
Roughly 10,000 job offers collected in 3 hours. Reverse engineering of ATS patterns from job descriptions and application forms.
Highlights
- Roughly 10,000 job offers collected in 3 hours.
- Reverse engineering of ATS patterns from job descriptions and application forms.
- 10 interviews obtained from only 15 highly personalized emails.
- 5 interviews came from offers whose official deadline had already closed.
- Automated ranking of opportunities where my profile had the highest match.