Project
AI and Employment Opportunity Restructuring
An automated analysis of 30K job postings across education, skills, industries, regions, salary, fairness, and automation risk.
Python · pandas · Visualization · Reporting · Labor Data
Product Snapshot
- Role
- Independent data analyst
- Users
- Job seekers, educators, and researchers studying AI-related labor-market change.
- Stage
- Automated cleaning, analysis, visualization, and Markdown reporting completed in 2025.11.
- Focus
- Education, skills, industry and regional differences, salary, fairness, and automation risk.
- Validation
- Turned 30,000 job postings into a reproducible analysis pipeline and structured report.
- Public Proof
- Data-processing code, visual outputs, and generated report.
Next Step
Research goal
Instead of asking only whether AI will replace jobs, this project examined how opportunity is redistributed: which roles raise skill thresholds, where demand concentrates, and how salary and automation risk move together.
Method
I built an automated workflow for cleaning, feature derivation, grouped analysis, visualization, and report generation across 30,000 job postings.
Result and boundary
The project produced reproducible scripts and a structured Markdown report. It describes correlations in a posting sample; job descriptions are not treated as observed employment outcomes, and risk scores are not deterministic forecasts of job disappearance.
Configure the public Giscus environment variables to open discussion on the public site.