(1) You could focus on building data processing platforms using, e.g., Spark. This will get you very close to the data science folks and you could probably end up doing some interdisciplinary work if you wanted it and demonstrated enough interest and competence. At the very least, people who can build highly scalable data processing systems and who also have a reasonable understanding of how the data is being used are very valuable.
(2) There are lots of companies out there that don't engage in data science/machine learning at all. You could join such a company and represent the push towards developing a data science or ML division or team. If you're successful this could also get you major credit as a manager as well as putting you very close to real-world data scientists and ML projects.