Article Abstract: Despite the wealth of information available to job seekers, choosing careers and transitioning between jobs remain a complex endeavor. With millions of job titles available, it is difficult for candidates to know what each role entitles and how well-suited they are for various open positions. Our research aims to break through this complexity and provide a quantitative framework for recommending job transitions.

We use a comprehensive job and skill data set with annotated variables (job activity, job seniority) in conjunction with a multivariate technique to create a matching score for any job pair (A, B). The multivariate combination relies on the similarity between the titles of jobs A and B, their descriptions, and their required skills. This model is used to determine the top 10 recommendations for each of the 3,190 jobs in the data set considered for this analysis.

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This article contributes to the broader collection of external ESCO publications, showcasing the use of ESCO within various methodologies or its presentation in both European and International contexts. As ESCO becomes increasingly used in applications and research projects across Europe and beyond, it is valuable to collect such sources and share best practices by diverse stakeholders. Therefore, this collection of external publications strengthens the exchange of knowledge within the ESCO community and can contribute to mutual learning in the field of skills, occupations and qualifications among European and international actors. If you are interested in sharing your publication, please write to EMPL-ESCO-SECRETARIAT@ec.europa.eu