About the role
Learn how to automate job searching using Python: scrape hundreds of jobs, filter efficiently, and find the perfect role faster.
The average job seeker spends 11 hours per week searching for jobs, according to LinkedIn. For tech roles, it's even worse, you're dealing with hundreds of postings across multiple platforms. When my partner started her job search, she was spending hours daily just scrolling through LinkedIn. There had to be a better way.
The Challenge
For Web developers, the market is overwhelming. A single search for "Frontend Developer" in London returned 401 results. Each posting requires:
5 seconds to review the title
3–4 clicks to view details
30–60 seconds to scan requirements
Manual copy-pasting to track interesting roles
Constant tab switching and back-navigation
For 401 jobs, that's hours of pure mechanical work!
The Solution: Automation Pipeline
I built a three-step automation pipeline that cut the process down to 10 minutes:
Scrape job data using Python
Filter in bulk using Google Sheets
Review only the most promising matches
Step 1: Smart Scraping
I used JobSpy as the base and built JobsParser to handle:
CLI
Rate limiting (to avoid LinkedIn blocks)
Retry logic for failed requests
Here's how to run it:
pip install jobsparser
**Bonus:** search manually on LinkedIn the number of results for your search term and use it for the ` — results-wanted` parameter.
jobsparser \
--search-term "Frontend Developer" \
--location "London" \
--site linkedin \
--results-wanted 200 \
--distance 25 \
--job-type fulltime
If `jobsparser` is not in your path, you can run it as a module directly:
python -m jobsparser \
--search-term "Frontend Developer" \
--location "London" \
--site linkedin \
--results-wanted 200 \
--distance 25 \
--job-type fulltime
The output is a CSV with rich data:
Job title and company
Full description
Job type and level
Posted date
Direct application URL
JobSpy and JobsParser are also compatible with other job boards like LinkedIn, Indeed, Glassdoor, Google & ZipRecruiter.
Step 2: Bulk Filtering
While pandas seemed obvious (and I've given it a fair try), Google Sheets proved more flexible. Here's my filtering strategy:
Time Filter: Last 7 days
Jobs older than a week have lower response rates
Fresh postings mean active hiring
Time Filter
Experience Filter: "job_level" matching your experience:
For my partner who is looking for her first role, I filtered:
"Internship"
"Entry Level"
"Not Applicable"
Experience Filter
Tech Stack Filter: "description" contains:
The word "React"
Description Filter
More complex filters can be created to check for multiple technologies.
This cut 401 jobs down to 8 matches!
Step 3: Smart Review
For the filtered jobs:
Quick scan of title/company (10 seconds)
Open promising job_url in new tab
Check the description in detail.
