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Research on Employment Status and Talent Segmentation in Data Science

In: Liss 2023

Author

Listed:
  • Xiaoling Xiao

    (University of Science and Technology Beijing)

  • Huixia He

    (University of Science and Technology Beijing)

  • Sen Wu

    (University of Science and Technology Beijing)

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.

Suggested Citation

  • Xiaoling Xiao & Huixia He & Sen Wu, 2024. "Research on Employment Status and Talent Segmentation in Data Science," Lecture Notes in Operations Research, in: Daqing Gong & Yixuan Ma & Xiaowen Fu & Juliang Zhang & Xiaopu Shang (ed.), Liss 2023, pages 844-854, Springer.
    • Handle: RePEc:spr:lnopch:978-981-97-4045-1_66
    DOI: 10.1007/978-981-97-4045-1_66
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