Optimize High-Recall Indeed Job Scraper (Python, Multithreading)

Job ID: 39745143

Budget: ₹2,000 – ₹2,001 INR

Optimize High-Recall Indeed Job Scraper (Python, Multithreading)

Description:
I have a Python script that scrapes job listings from Indeed USA using ZIP codes as input. It’s built around the external library and reads ZIPs from an .xlsx file covering all US states. The scraping logic is solid—it fetches a high volume of jobs posted in the last 24 hours with excellent accuracy.

I’ve tested it for California only:

8,000+ jobs fetched from 163 ZIP codes

Took 6–7 hours to complete

California has 1,200+ ZIPs, and I haven’t even touched other states yet

What I Need:
I’m looking for a Python expert to optimize the script for speed without compromising the current scraping logic. The goal is to make it scalable across all states and ZIP codes.

Optimization Goals:
Implement multithreading or multiprocessing to parallelize ZIP-level and/or page-level requests

Maintain the current adaptive radius and job-type split logic

Support proxy rotation if needed (I dont have proxies now)

Preserve deduplication and error handling

Ideally stream results to CSV shards or a single output file

I will share the code, once we finalize