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The impact of person-job fit on job satisfaction and performance is widelyacknowledged, which highlights the importance of providing workers with nextsteps at the right time in their career. This task of predicting the next stepin a career is known as career path prediction, and has diverse applicationssuch as turnover prevention and internal job mobility. Existing methods tocareer path prediction rely on large amounts of private career history data tomodel the interactions between job titles and companies. We propose leveragingthe unexplored textual descriptions that are part of work experience sectionsin resumes. We introduce a structured dataset of 2,164 anonymized careerhistories, annotated with ESCO occupation labels. Based on this dataset, wepresent a novel representation learning approach, CareerBERT, specificallydesigned for work history data. We develop a skill-based model and a text-basedmodel for career path prediction, which achieve 35.24% and 39.61% recall@10respectively on our dataset. Finally, we show that both approaches arecomplementary as a hybrid approach achieves the strongest result with 43.01%recall@10.

Career Path Prediction using Resume Representation Learning and Skill-based Matching