Learning to leverage CM tools for the design of ephemeral, experimental, analytical infrastructure projects was a key turning point in my career. It reinvigorated my love for R and data science, and introduced me to the world of DevOps. As more data scientists produce products or artifacts that go on to live in production, it is important to help them understand DevOps as well as how an analytic toolchain fits into the traditional technology value stream. My job is to help data science and IT/Ops groups communicate and establish shared goals. I would like to share some of the strategies and lessons learned from these conversations.