How to find a perfect data scientist : a distance-metric learning approach
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...
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| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/103255 http://hdl.handle.net/10220/47276 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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