通过爬取招聘数据，实时分析半导体行业人才需求与技能错配。
JobPulse: A Big Data Approach to Real-Time Engineering Workforce Analysis and National Industrial Policy
- 从公开招聘信息中提取需求数据，构建半导体行业人才需求画像。
- 揭示南加州半导体工程岗位分布及各职能需求比例，发现高端研发岗位缺口显著。
- 为政府制定产业政策提供近实时的人才供需洞察，适合政策制定者参考。
就业对国家和全球事务影响深远，而职位空缺与失业率估算对金融市场和政府决策至关重要。然而，现有报告多聚焦求职者供给端，对技能错配的理解不足。招聘平台上的职位信息反映了市场需求，但以往研究多关注候选人简历中的技能描述。本文提出一种大数据方法，聚焦于公开职位发布的需方信息，利用商业网络爬虫与新型数据处理流程，构建了美国半导体行业的职位数据集，重点覆盖南加州这一先进科技中心。研究揭示了雇主主体结构及各类岗位的相对需求情况。工作在三个层面作出贡献：首先，实现近实时的劳动力需求洞察；其次，探讨大规模雇主数据库分析中的消歧与语义挑战；最后，呈现南加州半导体工程生态系统现状。
原文摘要 · Abstract (English)
Employment on a societal scale contributes heavily to national and global affairs; consequently, job openings and unemployment estimates provide important information to financial markets and governments alike. However, such reports often describe only the supply (employee job seeker) side of the job market, and skill mismatches are poorly understood. Job postings aggregated on recruiting platforms illuminate marketplace demand, but to date have primarily focused on candidate skills described in their personal profiles. In this paper, we report on a big data approach to estimating job market mismatches by focusing on demand, as represented in publicly available job postings. We use commercially available web scraping tools and a new data processing scheme to build a job posting data set for the semiconductor industry, a strategically critical sector of the United States economy; we focus on Southern California as a central hub of advanced technologies. We report on the employer base and relative needs of various job functions. Our work contributes on three fronts: First, we provide nearly real-time insight into workforce demand; second, we discuss disambiguation and semantic challenges in analysis of employer data bases at scale; and third, we report on the Southern California semiconductor engineering ecosystem.
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