Automated Human Capital Management System

Finding a meaningful and fulfilling work and finding right talent for a given job is a challenging and classical Human Capital Management (HCM) problem. For a long time, HCM was carried out in an offline manner requiring human interventions. As scale of organizations have grown, it becomes relevant to automate these processes. In this paper, we present and detail a stateless scalable architecture for an automated HCM system. For clustering and categorizing job postings and candidate profiles we present algorithms that uses Machine Learning techniques. We present an algorithm that uses Natural Language Processing for feature extraction. We also present a ranking algorithm that uses semantic web technologies for providing accurate recommendations. We conclude this paper presenting results of presented algorithms on real-world job posting and candidate data.