Automate your job search: a daily LinkedIn jobs pipeline in Python
Job hunting has a timing problem that nobody warns you about. A posting goes up. For the first few hours it has a handful of applicants. By day three it has two hundred, a recruiter has stopped reading carefully, and your carefully tailored application is row 187 in an ATS. You cannot control how good the other 186 candidates are. You can control whether you were row 8 instead. That is an…
Job hunting presents a timing issue that goes unnoticed. After a posting appears, it garners a few applications in the initial hours. By the third day, it attracts two hundred applicants, causing a recruiter to overlook your personalized application at position 187 in the Application Tracking System (ATS). While you have no control over the quality of other candidates, you can influence your rank in the ATS. This situation highlights an automation problem that requires only forty lines of Python code to resolve.
LinkedIn's job search offers numerous customization options. For this specific purpose, two key filters significantly impact the outcome: postings from the past 24 hours and listings with fewer than 10 applicants. By leveraging these two filters, you can create an edge over other job seekers. While other factors like seniority, remote work, and job type narrow down the suitable roles, the two aforementioned filters are crucial to increasing your chances of being read and considered.
It is impractical to manually check these filters daily. Therefore, let's automate this process using Python.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
