Employers’ Requirements for Data Scientists - an Analysis of Job Posts

Monica Mihaela Maer Matei () and Anamaria Beatrice Aldea ()
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Monica Mihaela Maer Matei: National Scientific Research Institute for Labour and Social Protection
Anamaria Beatrice Aldea: Researcher, National Scientific Research Institute for Labour and Social Protection, Bucharest

Logos Universalitate Mentalitate Educatie Noutate - Sectiunea Stiinte Economice si Administrative/ Logos Universality Mentality Education Novelty - Section: Economical and Administrative Sciences, 2019, vol. 4, issue 1, 21-32

Abstract: Technological development and innovation are the main drivers of jobs transformations leading to skill mismatch. One very dynamic domain, dealing with these issues is data science. Generally, a data scientist has to work with big data in a scientific and creative manner. To reduce the drawbacks of a sparse matching between educational offer and the new requirements of the labour market is essential to understand real time job market requirements. The most relevant data source for such an investigation is represented by online job market portals. Nowadays, with the increasing digitalisation of society, these portals are considered to improve transparency and signalling in labour markets. Moreover, the potential of the textual vacancy data from Romanian online recruiting platforms has not been exploited up to now. Following these arguments, in order to understand employers’ requirements for data science jobs in Romania, we develop an analysis of textual data extracted from job advertisements dedicated to data scientists. Mainly the data analysis will involve the investigation of term frequencies and associations combined with relevant visualization tools. The research will reveal the employers’ needs and will support training providers like universities to adapt curricula and training programmes so that they provide what the labour market requires. Moreover, the findings of this research could support young people in making better training choices, signal important trends related to occupations and skills.

Keywords: labor market; data scientist; text mining; job posts (search for similar items in EconPapers)
JEL-codes: A23 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:lum:rev14e:v:4:y:2019:i:1:p:21-32

DOI: 10.18662/lumeneas/10

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