Article

Clustering AI Job Roles Using PCA and K-Means Based on Skill Profiles and Automation Risk

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Abstract

The rapid expansion of artificial intelligence (AI) technologies has significantly transformed the job market, leading to emerging demands for hybrid skillsets and raising concerns over automation-induced job displacement. This study aims to identify meaningful patterns within the AI job landscape by clustering job roles based on required skills and automation risk. Using a dataset of 500 AI-related job entries, we applied Principal Component Analysis (PCA) to reduce the dimensionality of the skill space, followed by K-Means clustering to group similar roles. The analysis revealed four distinct clusters with notable differences in salary, skill emphasis, and automation vulnerability. Further examination showed that roles emphasizing technical competencies—such as Python, Machine Learning, and Data Analysis—tend to fall into higher-paying clusters with lower automation risk.  In contrast, jobs requiring predominantly soft skills—such as Communication, Marketing, and Sales—are more susceptible to automation and are generally lower-paid. Correlation analysis confirmed these trends, with technical skills showing strong negative correlations with automation risk, while non-technical skills demonstrated positive correlations. These findings underscore the growing importance of technical proficiency in securing resilient careers in the AI sector, offering strategic insights for education, workforce development, and policy formulation.

Keywords: Artificial Intelligence, Job Market, K-Means Clustering, Principal Component Analysis, Automation Risk, Skill Analysis, Workforce Resilience

How to Cite:

Rahardja, U. & Aini, Q., (2025) “Clustering AI Job Roles Using PCA and K-Means Based on Skill Profiles and Automation Risk”, Artificial Intelligence in Learning 1(4), 315-328. doi: https://doi.org/10.63913/ail.v1i4.44