<![CDATA[The telecommunications industry relies significantly on the knowledge and contributions of engineers, who play a crucial role in designing, enhancing, and supervising diverse communication infrastructure systems. As engineers gain experience, they frequently advance to supervisory or consulting positions in which they provide expert guidance. Many businesses have delegated their recruitment processes to contract workers to optimize operational costs and boost productivity. It is essential to evaluate the performance of outsourced employees in order to determine the value they add to the organization. Performance evaluations play a vital role in evaluating factors such as attendance, skill level, burden management, and job performance. These evaluations aid in contract renewal decision-making. Various metrics emphasizing factors such as effective skills, well-defined objectives, and regular status updates can be used to determine the success of outsourced employees. This permits a comprehensive evaluation and the identification of improvement areas. In this paper, the investigation concentrates on using cluster analysis techniques, specifically partition-based algorithms such as K-means, to classify outsourced workers according to their individual skills and characteristics. Then, we compare the provided insights with the density-based algorithm DBSCAN to comprehend them. Cluster analysis is a potent method for analyzing large datasets, allowing for swift and confident conclusions. By utilizing cluster analysis, organizations can gain insight into the diverse skill sets and characteristics of their outsourced workforce, thereby improving resource allocation, task assignment, and management as a whole]]>
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