Data Scientist Selection in IT Industry: SMCDM Approach

Abstract

Traditional Multi-Criteria Decision Making (MCDM) methods cannot provide solutions to problems that may be encountered in the near future. Basically, MCDM methods have a very rich algorithm literature for decision making problems. Traditional MCDM methods do not take short-term changes into account. In a new development, it is necessary to start the process from the beginning and solve the problem from the beginning. This leads to huge loss of money and time. The purpose of developing the MCDM method is to make decision-making problems more efficient and to prevent losses in the process. However, the MCDM method is a method that has been developed based on the changes in the process, which is a problem that has not been addressed until now. Since it will be tiring and difficult to solve the problem by assigning new weights from the beginning, the Stratified Multi-Criteria Decision Making (SMCDM) Method has been developed for the possibilities that may occur in the near future. In this study, an exemplary study of the SMCDM method was carried out in a IT company. In the data scientist selection problem, the best alternative was selected with the SMCDM method, taking into account the events that may occur in the near future.