Introduction Fast-emerging technologies are making the job market dynamic, causing desirable skills to evolve continuously. It is therefore important to understand the transitions in the job market to proactively identify skill sets required. Case description A novel data-driven approach is developed to identify trending jobs through a case study in the oil and gas industry. The proposed approach leverages a range of data analytics tools, including Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), Factor Analysis and Non-Negative Matrix Factorization (NMF), to study changes in the market. Further, our approach is capable of identifying disparities between skills that are covered by the educational system, and the skills that are required in the job market. Discussion and evaluation The results of the case study show that, while the jobs most likely to be replaced are generally low-skilled, some high-skilled jobs may also be at risk. In addition, mismatches are identified between skills that are imparted by the education system and the skills required in the job market. Conclusions This study presents how job market and skills required evolved over time, which can help decision-makers to prepare the workforce for highly demanding jobs and skills. Our findings are in line with the concerns that automation is decreasing the demand for certain skills. On the other hand, we also identify the new skills that are required to strengthen the need for collaboration between minds and machines.

The COVID-19 pandemic has significantly affected all spheres of life, including the healthcare workforce. While the COVID-19 pandemic has started driving organizational and societal shifts, it is vital for healthcare organizations and decision-makers to analyze patterns in the changing workforce. In this study, we aim to identify patterns in healthcare job postings during the pandemic to understand which jobs and associated skills are trending after the advent of COVID-19. Content analysis of job postings was conducted using data-driven approaches over two-time intervals in the pandemic. The proposed framework utilizes Latent Dirichlet Allocation (LDA) for topic modeling to evaluate the patterns in job postings in the US and the UK. The most demanded jobs, skills and tasks for the US job postings are presented based on job posting data from popular job posting websites. This is obtained by mapping the job postings to the jobs, skills and tasks defined in the O*NET database for the healthcare occupations in the US. The topic modeling results clearly show increased hiring for telehealth services in both the US and UK. This study also presents an increase in demand for specific occupations and skills in the USA healthcare industry. The results and methods used in the study can help monitor rapid changes in the job market due to pandemics and guide decision-makers to make organizational shifts in a timely manner.