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Title: How to find a perfect data scientist : a distance-metric learning approach
Authors: Hu, Han
Luo, Yong
Wen, Yonggang
Ong, Yew-Soon
Zhang, Xinwen
Keywords: Natural Language Processing
DRNTU::Engineering::Computer science and engineering
Data Scientist
Issue Date: 2018
Source: Hu, H., Luo, Y., Wen, Y., Ong, Y.-S., & Zhang, X. (2018). How to find a perfect data scientist : a distance-metric learning approach. IEEE Access, 6, 60380-60395. doi:10.1109/ACCESS.2018.2870535
Series/Report no.: IEEE Access
Abstract: The title of data scientist has been described as one of the sexiest jobs of the 21st century. Numerous efforts have been made to define the job of a data scientist in a qualitative manner by, for example, listing the job functions and required skill sets of data scientists. However, to the best of our knowledge, no attempt has been made to define the term data scientist in a scientific manner. In this paper, we address this issue by using a data-driven approach to answer three questions: 1) What is a proper definition of the term data scientist from a market-demand perspective? 2) Do self-described data scientists meet the market demand? and 3) Finally, how can companies efficiently recruit data scientists that match their openings? To answer these questions, we crawl two data sets for the supply and demand sides. For the former, we collect a set of data scientist user profiles from LinkedIn; for the latter, we collect a set of data scientist job descriptions from Monster. We first parse the set of data scientist job descriptions via natural language processing techniques and derive a scientific definition of the job of a data scientist via a clustering algorithm. Second, we use the same approach to determine that, under the aforementioned definition, self-claimed data scientists on the market would meet the market demand with a high probability. Finally, we introduce a distance-metric learning approach that can be used by companies to find data scientist candidates that match their openings. We achieve an average precision of 12.31%; i.e., one in ten candidates with matching qualifications would accept a given offer. The application of this quantitative approach could significantly reduce the human-resource costs incurred by companies in recruiting matching data scientists.
DOI: 10.1109/ACCESS.2018.2870535
Rights: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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