Abstract
Understanding structural dynamics and workforce segmentation has become essential for sustainable economic policy and employment regulation in the context of global economic transformation, technological change, and labour market volatility. The goal of the current study is to identify key worker groups with varying levels of employment stability, staff turnover, and personnel cost distribution. The fuzzy clustering algorithm (Fuzzy C-Means) is used as the primary method, which makes it possible to assign objects to several clusters with varying degrees of affiliation. The analysis was based on official statistics of the labor market in the Republic of Kazakhstan for the period 2013-2023. The analysis revealed three stable clusters: stable workforce flow cluster with low staff turnover (0.172) and balanced replacement (0.152); adaptive cluster with moderate instability, turnover of 0.355 and demand for labor up to 30 020 people; unbalanced cluster with high-cost variability (up to 19 billion tenge), compensation (coefficient of variation of 0.386) and maximum admission of graduates (up to 86,227 people). Kazakhstan's labor market is characterized by stability in traditional sectors, moderate adaptation in cyclical industries, and high volatility in technologically advanced and competitive segments. These results suggest the need for a differentiated employment policy that considers industry-specific characteristics and the digital transformation of the economy. Future research should investigate sector-specific models of human resource transformation and explore how employment dynamics can be integrated into strategies for sustainable and innovation-driven development.