One of the most dynamically changing parts of the labor market relates to information technologies. Skillsets demanded by employers in this sphere vary across different industries, organizations and even certain vacancies. The educational system in the most cases lags behind such changes, so that obsolete skillsets are being taught. This article proposes an algorithm of skillsets identification that allows us to extract skills that are needed by companies from different occupational groups in the information technologies sector. Using the unstructured online-vacancies database for the Russian regional labor market, skills are extracted and unified with the use of TF-IDF and n-grams approaches. As a result, key specific skillsets for various occupations are found. The proposed algorithm allows us to identify and standardize key skills which might be applicable to create a system of Russian classification for occupations and skills. In addition, the algorithm allows us to provide lists of the key combinations of skills that are in high demand among companies inside each particular occupation.

Labor market transformations significantly affect the sphere of information technologies (IT) introducing new instruments, architectures, and frameworks. Employers operate with new knowledge domains which demand specific competencies from workers including combinations of both technical (“hard”) and non-technical (“soft”) skills. The educational system is now required to provide the alumni with up-to-date skill sets covering the latest labor market trends. However, there is a big concern about the self-adaptation of educational programs for meeting the companies’ needs. Accordingly, frequent changes in job position requirements call for the tool for in-time categorization of vacancies and skills extraction. This study aims to show the demand for skills in the IT sphere in the Commonwealth of Independent States (CIS) region and discover the mapping between required skill sets and job occupations. The proposed methodology for skills identification uses natural language processing, hierarchical clustering, and association mining techniques. The results reveal explicit information about the combinations of “soft” and “hard” skills required for different professional groups. These findings provide valuable insights for supporting educational organizations, human resource (HR) specialists, and state labor authorities in the renewal of existing knowledge about skill sets for IT professionals. In addition, the provided methodology for labor market monitoring has a high potential to ensure effective matching of employees.

The paper discusses the problem of aligning the professional standards with a real demand in the labor market of top management. The study proposes approaches to the analysis of demand in the labor market and algorithms for quantification of qualitative information about relevant skills, as well as the methodology for grouping professional standards and job advertisements. The research methods of text mining, topic modeling and cluster analysis were applied for the analysis of databases of vacancies and the professional standards. As a result, data on the labor market were summarized as 30 professional groups; each group is characterized by a specific set of competencies. The conclusions present the correspondence between the classification of labor market and the current need in personnel and areas of their discrepancy with respect to the matrix of skills for subgroups of professional standards and vacancies of the HeadHunter portal in the “Higher management” category are identified. The proposed approaches and methods allow to flexibly classify various professions between official classifiers and the demand in the labor market. It helps to improve the coordination between educational organizations and professional communities in order to ensure consistency of requirements for workers.

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Purpose This study aims to investigate the relationship between the demand on “soft” skills and suggested salaries for IT specializations in Russia. Design/methodology/approach Based on the database of vacancies, econometric modeling and cluster analysis of job occupations are implemented. Findings The results show positive association between demand for “soft” skills and wage if the model is controlled for the working experience and narrow professional occupations. Findings provide evidence that there is significant wage premium for “soft” skills in cases when job positions either imply no experience or require specialists with at least three years of tenure. Originality/value This research provides new evidence on the relationship between “soft” skills and wage using job postings data from Russia. This paper identifies the presence of wage premium for “soft” skills among IT specialists if controlling for sub-specializations, year, region and working experience. The robustness checks indicate no significant changes in the obtained results.