JobScan AI Scraper

A streamlined tool that pulls fresh job postings from multiple sources and unifies them into clean, structured data. It helps you cut through clutter, spot meaningful openings fast, and power up your job research or automation workflows.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Introduction

This scraper collects job listings from Google results and major job boards, arranges them neatly, and delivers consistent structured output. It solves the messy problem of inconsistent job formats spread across different sites. It’s handy for researchers, developers, job seekers, or anyone who needs reliable job data at scale.

How It Works Behind the Scenes

• Searches multiple job boards and Google in parallel.
• Filters results using optional and mandatory keywords.
• Restricts listings by date range to surface only recent posts.
• Normalizes fields like role, company, salary, and work model.
• Delivers results in consistent JSON ready for processing.

Features

Feature	Description
Custom search logic	Supports AND/OR keyword logic to dial in relevance.
Multi-site scraping	Pulls listings from several job platforms at once.
Structured output	Produces normalized JSON ideal for analysis or pipelines.
Date filtering	Limits scraping to recent postings only.
Scalable processing	Handles large result batches efficiently.

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What Data This Scraper Extracts

Field Name	Field Description
company_name	The hiring company or organization.
role_name	The job title listed in the posting.
job_description	Summary of responsibilities and tasks.
requirements	Key skills or qualifications extracted from the post.
salary	Listed compensation, if available.
employment_type	Type of employment, such as full-time or contract.
remote	Work model: onsite, hybrid, or remote.
location	City or region of the role.
country	Country in which the job is located.
publish_date	When the listing was published.
viewed_date	When the scraper captured the listing.
url	Direct link to the job posting.

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Example Output

[
    {
        "company_name": "Siemens EDA (Siemens Digital Industries Software)",
        "role_name": "Software Engineer - AI/ML",
        "job_description": "Design and develop AI/ML-driven algorithms and solutions to enhance simulation tools. Integrate machine learning techniques into simulation and verification workflows.",
        "requirements": "C/C++;Python;AI/ML;Linux;UNIX",
        "salary": "$105,100.00/yr - $189,200.00/yr",
        "employment_type": "full-time",
        "remote": "hybrid",
        "location": "Austin, TX",
        "country": "USA",
        "publish_date": null,
        "url": "https://www.linkedin.com/jobs/view/software-engineer-ai-ml-at-siemens-eda-siemens-digital-industries-software-4134835705",
        "viewed_date": "2025-01-28"
    }
]

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Directory Structure Tree

JobScan AI/
├── src/
│   ├── index.js
│   ├── search/
│   │   ├── keyword_filter.js
│   │   └── date_filter.js
│   ├── extractors/
│   │   ├── google_parser.js
│   │   ├── jobboard_parser.js
│   │   └── normalize.js
│   ├── utils/
│   │   ├── request.js
│   │   └── logger.js
│   └── config/
│       └── schema.json
├── data/
│   ├── sample_output.json
│   └── inputs.sample.json
├── package.json
└── README.md

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Use Cases

• Market analysts use it to collect job posting patterns, so they can study hiring trends across industries.
• Job seekers use it to instantly surface roles that match their skills, so they avoid manually checking scattered job boards.
• Recruitment teams use it to track competitor hiring activity, so they gain insight into shifts or talent demand.
• Developers integrate it into pipelines to automate job-related data ingestion for dashboards or monitoring tools.
• Career coaches gather curated job sets for clients, so they can provide personalized recommendations.

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FAQs

Does this scraper work with any job site? It targets job boards commonly indexed by Google along with several major platforms. If a specific site is difficult to parse, the configuration or filters may need adjusting.

Can I limit the number of results? Yes. A result limit parameter lets you cap how many listings are returned per search cycle.

What happens if a listing doesn’t include fields like salary or publish date? Missing fields are returned as null, keeping the output format consistent.

Is the scraper suitable for large-scale usage? It’s designed to handle high-volume searches efficiently, though extremely large runs may require tuning keyword sets and limits.

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Performance Benchmarks and Results

Primary Metric: Processes batches of 500–1,000 job listings per run with steady retrieval speed across multiple job boards.

Reliability Metric: Maintains over 95% success rate in collecting accessible job posting URLs even across mixed platforms.

Efficiency Metric: Optimized filters reduce unnecessary requests, keeping average resource usage low during multi-site searches.

Quality Metric: Consistently captures 90%+ of structured fields such as company, role, location, and requirements with minimal data loss.

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