The increasing importance of Data Science is causing companies to re-evaluate their personnel requirements. At the same time, however, it is unclear which tasks, knowledge, skills and abilities make up this field. With this in mind, this project researched, defined, and characterized the heterogeneous job roles in Data Science based on 11,402 job postings from online job platforms such as Indeed, Monster, and Glassdoor, as well as interviews with job role holders from industry. The findings help companies better understand the heterogeneous job role landscape in order to more effectively leverage Data Science with the right personnel.
The purchasing departments of companies are responsible, among other things, for reducing the number of suppliers and usually consider various selection criteria. Their decisions are based on accessing and analyzing large amounts of data from various source systems. However, buyers usually lack the necessary technological and analytical knowledge as well as suitable tools to do this effectively. In this project, we therefore developed a self-service analytics system to support buyers in this task. Based on a machine learning model we trained, the system recommends appropriate reallocations of purchasing volume between suppliers. The results show how self-service analytics can be used to help user groups with less technological and analytical knowledge to use machine learning models more effectively.