Risk is one of the most crucial components of a project. Its proper evaluation and treatment increase the chances of a project's success. This article presents the risks in Data Science projects, assessed through a study conducted with the Delphi technique, to answer the answer the question, "What are the risks of Data Science projects". The study allowed the identification of specific risks related to data science projects, however it was possible to verify that over a half of the most mentioned risks are similar to other types of IT projects. This paper describes the research from expert selection, risk identification and analysis, and the first conclusions.info:eu-repo/semantics/publishedVersio
