Business world is getting increasingly dynamic. Information processing using knowledge-, service-, and cloud-based systems makes the use of complex, dynamic and often knowledge-intensive activities an inevitable task. Knowledge-intensive processes contain a set of coordinated tasks and activities, controlled by knowledge workers to achieve a business objective or goal. Recruitment process - i.e., the process of attracting, shortlisting, selecting and appointing suitable candidates for jobs within an organization - is an example of a knowledge-intensive process, where recruiters (i.e., knowledge workers who have the experience, understanding, information, and skills) control various tasks from advertising positions to analyzing the candidates’ Curriculum Vitae. Attracting and recruiting right talent is a key differentiator in modern organizations. In this paper, we put the first step towards automating the recruitment process. We present a framework and algorithms (namely iRecruit) to: (i) imitate the knowledge of recruiters into the domain knowledge; and (ii) extract data and knowledge from business artifacts (e.g., candidates’ CV and job advertisements) and link them to the facts in the domain Knowledge Base. We adopt a motivating scenario of recruitment challenges to find the right fit for Data Scientists role in an organization.