Job Market Research & Recruitment Intelligence — Scrape Job Board Data

Salary data accuracy depends on disclosure rates, which vary by region and platform. In the US, salary disclosure is increasingly common due to transparency legislation in states like New York and California — meaning a growing percentage of job postings now include salary ranges. Where salary ranges are disclosed, the data is highly accurate as it reflects the employer's actual offer range. For best results, analyze median salary ranges across a large sample (50+ postings) for a given role and location rather than relying on individual postings.

Yes — by running weekly scrapes and comparing job IDs or posting titles across runs, you can infer time-to-fill metrics for specific companies and roles. Jobs that appear in multiple consecutive weekly scrapes are "open" for that duration. This data is valuable for understanding which roles are hardest to fill in your market, and for identifying companies struggling to recruit (a signal of strong staffing need or poor employer branding).

Yes. All major job boards include work arrangement as a searchable and extractable field. You can configure OneScraper's job board scrapers to target only remote positions, only on-site, or only hybrid roles. This filter is particularly useful for workforce analysts studying the geographic distribution of remote work opportunities, or HR teams benchmarking remote vs. on-site salary premiums in their industry.

Job postings are public information displayed to all website visitors. Extracting publicly available job data for research and analysis purposes is widely considered legal. The hiQ v. LinkedIn Ninth Circuit ruling specifically addressed public data scraping and affirmed that accessing publicly visible information does not constitute unauthorized access under the Computer Fraud and Abuse Act. That said, individual platform terms of service should be reviewed, and OneScraper recommends using scraped data for research and analysis rather than reproducing or redistributing raw job posting content.

The workflow is straightforward: scrape a large sample of job postings (500+) for your target role category, export the job description text to a spreadsheet, and use text analysis to count keyword frequency. Skills mentioned most frequently across postings are currently in highest demand. By comparing this analysis across monthly scrapes, you can track which skills are growing in mention frequency over time — these are the skills with increasing market demand. Alternatively, pass the job descriptions to a GPT API prompt asking it to extract and rank the skills listed.