Determination of Key Engineering Competency Attributes by Evaluating Resumes Using Machine Learning
Open AccessGovernment agencies are having difficulty identifying qualified engineering job candidates from internal and external sources. Many organizations have complex engineering competency models that describe their desired knowledge, skills, and abilities required to succeed as an engineer. The aim of this research is to reduce the number of key attributes necessary to evaluate the resume of a senior engineering position candidate. Starting with a large, combined set of attributes, several machine learning algorithms were trained to determine if an applicant is minimally qualified for the engineering position based upon their resume. The results of the algorithm were compared to the qualification decision made by a panel of senior hiring managers. The final set of attributes and trained machine learning algorithm can then be used by hiring managers to quickly filter a set of resumes from job applicants or database to create a list of qualified candidates to advance in the hiring process. The reduced set of attributes can be used to improve searches in existing databases and to enhance future job announcements. This methodology can be applied to a variety of position experience levels and specialties as well as to organizations outside the government. The starting list of competencies can be pulled from the organization’s existing models and/or job descriptions and those can be reduced to those that contribute the most to successfully classifying job candidates.
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