Data science and machine learning (DS/ML) are changing the way many people approach catalysis research, ranging from new design of experiment approaches, new methods in simulation, even new ways of interacting with the scientific literature. It is challenging today to even keep up with new work as it changes so quickly. In this talk, I will provide an overview of several ways we have incorporated DS/ML into our catalysis research, beginning with a state of the art large catalysis machine-learned model from the Open Catalyst project. I will then show how we have used concepts from DS/ML to develop new solutions to problems in chemical engineering using automatic differentiation. The takeaway messages from this talk will be that DS/ML is here to stay, and worth learning about. It is not a panacea solution though, with remaining challenges to address including educational, technical and communication challenges.