The recruitment of new personnel is one of the most essential business processes which affectthe quality of human capital within any company. It is highly essential for the companies toensure the recruitment of right talent to maintain a competitive edge over the others in themarket. However IT companies often face a problem while recruiting new people for theirongoing projects due to lack of a proper framework that defines a criteria for the selectionprocess. In this paper we aim to develop a framework that would allow any project manager totake the right decision for selecting new talent by correlating performance parameters with theother domain-specific attributes of the candidates. Also, another important motivation behindthis project is to check the validity of the selection procedure often followed by various bigcompanies in both public and private sectors which focus only on academic scores, GPA/gradesof students from colleges and other academic backgrounds. We test if such a decision willproduce optimal results in the industry or is there a need for change that offers a more holisticapproach to recruitment of new talent in the software companies. The scope of this work extendsbeyond the IT domain and a similar procedure can be adopted to develop a recruitmentframework in other fields as well. Data-mining techniques provide useful information from thehistorical projects depending on which the hiring-manager can make decisions for recruitinghigh-quality workforce. This study aims to bridge this hiatus by developing a data-miningframework based on an ensemble-learning technique to refocus on the criteria for personnelselection. The results from this research clearly demonstrated that there is a need to refocus onthe selection-criteria for quality objectives.
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