In recent years, Machine Learning (ML) components have been increasingly integrated into the core systems of organizations. Engineering such systems presents various challenges from both a theoretical and practical perspective. One of the key challenges is the effective interaction between actors with different backgrounds who need to work closely together, such as software engineers and data scientists. This work presents three studies investigating the current interaction and collaboration dynamics between these two roles in ML projects. Our first study depicts an exploratory case study with four practitioners with experience in software engineering and data science of a large ML-enabled system project. In our second study, we performed complementary interviews with members of two teams working on ML-enabled systems to acquire more insights into how data scientists and software engineers share responsibilities and communicate. Finally, our third study consists of a focus group where we validated the relevance of this collaboration during multiple tasks related to ML-enabled systems and assessed recommendations that can foster the interaction between the actors. Our studies revealed several challenges that can hinder collaboration between software engineers and data scientists, including differences in technical expertise, unclear definitions of each role s duties, and the lack of documents that support the specification of the ML-enabled system. Potential solutions to address these challenges include encouraging team communication, clearly defining responsibilities, and producing concise system documentation. Our research contributes to understanding the complex dynamics between software engineers and data scientists in ML projects and provides insights for improving collaboration and communication in this context. We encourage future studies investigating this interaction in other projects.