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