Developing a predictive analytical model to improve the recruitment efficiency of interns in ABC company

Abstract

" Businesses are operating in a volatile, competitive, unpredictable world with changes that happen more frequently than ever before. In a context like that, an organization needs to have an unstoppable and a definite purpose to survive through the chaos. An organization's talent or human resource becomes the most critical force which enables a successful survival. Currently, there is massive talent competition in the marketplace. Future-fit & purposeful talent is scarce; hence organizations fight to get the best talent to their organization, knowing the greater importance of having the right people in the right job. For organizations, it's not only about retaining the current workforce who are already employed, but it's critical to have a solid talent pool which is willing to be a part of your company in the future, as well. Recruitment & employer branding teams work day-in-day to build the required talent pool in the market, focusing on experienced talent & young talent by driving many initiatives. This study focuses on one of the initiatives (Mainly internship programmes) driven by the ABC company, operating in Sri Lanka and is a multinational company with over 80 years of heritage. The ABC company on board more than 50 interns within a year and has an excellent reputation as one of the best places for undergraduates to learn and gain exposure. However, the recruitment teams spend unwavering extended hours sorting this talent for more than two months per annum in all recruitment cycles. This study focuses on building a predictive application that enables the recruitment teams to reduce these two months to less than 1-2 weeks and simplifying the hiring process, allowing the team to have more valuable time to spend for management decision-making and more value adding work. Data was collected from the ABC company, and with the use of Natural Language Processing & Machine Learning, the pilot application was designed. Python Language was used to write the program, and open-source applications and packages were used"