Abstract
As Artificial Intelligence (AI) and Machine Learning (ML) capabilities expand across different industries, Human Resource (HR) professionals face the challenge of acquiring top talent to fill necessary vacancies within their organization. In data science, these vacancies differ across the private and federal sectors. Understanding which sector is more effective in communicating talent requirements has become imperative to maintain a competitive advantage in talent management. Thus, this research takes a novel approach to analyze job postings from the United States (US) federal government and US-based data science job postings within the private sector. We leverage a natural language processing model and term frequency-inverse document frequency (TF-IDF) analysis to analyze the job postings within our datasets. We then compare the results of our TF-IDF analysis to identify the representation of data science competencies within job postings and how the federal and private sectors differ.
Recommended Citation
Votto, Alexis M.; Manuel, Dylan; Valecha, Rohit; and Rao, H. Raghav, "Comparison of Federal and Private Sector Job Postings: A Data Science Term Frequency Analysis" (2022). Proceedings of the 2022 Pre-ICIS SIGDSA Symposium. 12.
https://aisel.aisnet.org/sigdsa2022/12
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