Research on Employment Status and Talent Segmentation in Data Science

Xiaoling Xiao (), Huixia He () and Sen Wu ()
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Xiaoling Xiao: University of Science and Technology Beijing
Huixia He: University of Science and Technology Beijing
Sen Wu: University of Science and Technology Beijing

A chapter in LISS 2023, 2024, pp 844-854 from Springer

Abstract: Abstract The rapidly advancing digital society is experiencing a significant scarcity of data science professionals, and there are numerous job opportunities emerging in the field of data science. Having a clear understanding of the employment landscape and talent distribution in this domain can greatly assist individuals seeking employment, yet the current theoretical research in data science lacks investigation in this area. Utilizing the most recent data science talent data, this study employs a combination of statistical analysis and cluster mining techniques to provide an overview of the employment landscape in data science, as well as the distinguishing characteristics of different types of professionals, taking into account industry conditions and internal segmentation. It outlines the employment trends observed within the field of data science and offers recommendations to job seekers regarding potential job opportunities, salary expectations, and benefits in this realm.

Keywords: data science; obtain employment; talent segmentation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-981-97-4045-1_66

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