Our research is aimed at applying a certain procedure developed by [1] that refers to the existing occupational settings and might successfully support in mining thorough and actionable information. The focus of our research is venture capital and private equity jobs, with opportunities for roles ranging from analysts and associates to directors. We tested and applied a method to pursue two distinct objectives. First, automate the process of sorting vacancies into occupational groups based on job titles by separating vacant positions based on the analysis of job descriptions that offer richer and more detailed information, although in a more unstructured fashion. Secondly, in order to better describe and profile skills in demand, the terms' occurrences are gathered in word vectors and visualized in intuitive and straightforward fashion. The procedure we followed starts with dataset exploration, which revealed an unsurprising variety in length, structure, and type of information, and data cleaning using replacement techniques on the "Job Title" and "Job Description" wordings. The next step is data preparation whose aim is to create a labeled dataset of examples. Data modelling for classification purposes (using K-Nearest Neighbors) is carried out to assign the labels quickly and efficiently in the cases where job titles are ambiguous. The final step is data visualization via word clouds that are a reasonable representation of job descriptions. Each distinct category includes a set of skills both of a functional nature (i.e. direct investment research) and of a soft one (i.e. team working, broad communication).

SKILL NEEDS FOR VENTURE CAPITAL AND PRIVATE EQUITY JOBS / Petroni, A.; Galati, F.; Carmignani, D.; Filippelli, S.. - (2018), pp. 3452-3458.

SKILL NEEDS FOR VENTURE CAPITAL AND PRIVATE EQUITY JOBS

A. Petroni;F. Galati;S. Filippelli
2018

Abstract

Our research is aimed at applying a certain procedure developed by [1] that refers to the existing occupational settings and might successfully support in mining thorough and actionable information. The focus of our research is venture capital and private equity jobs, with opportunities for roles ranging from analysts and associates to directors. We tested and applied a method to pursue two distinct objectives. First, automate the process of sorting vacancies into occupational groups based on job titles by separating vacant positions based on the analysis of job descriptions that offer richer and more detailed information, although in a more unstructured fashion. Secondly, in order to better describe and profile skills in demand, the terms' occurrences are gathered in word vectors and visualized in intuitive and straightforward fashion. The procedure we followed starts with dataset exploration, which revealed an unsurprising variety in length, structure, and type of information, and data cleaning using replacement techniques on the "Job Title" and "Job Description" wordings. The next step is data preparation whose aim is to create a labeled dataset of examples. Data modelling for classification purposes (using K-Nearest Neighbors) is carried out to assign the labels quickly and efficiently in the cases where job titles are ambiguous. The final step is data visualization via word clouds that are a reasonable representation of job descriptions. Each distinct category includes a set of skills both of a functional nature (i.e. direct investment research) and of a soft one (i.e. team working, broad communication).
978-84-09-05948-5
SKILL NEEDS FOR VENTURE CAPITAL AND PRIVATE EQUITY JOBS / Petroni, A.; Galati, F.; Carmignani, D.; Filippelli, S.. - (2018), pp. 3452-3458.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2855988
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