The global ocean plays a central role in maintaining the health of our planet and regulating various critical factors, such as heat and carbon levels, biological productivity, and sea level. Despite its importance, there are still unresolved questions regarding the driving forces behind even major circulation features, which limits our ability to monitor and understand ongoing changes. In this presentation, I will discuss the basin-scale circulation in the North Atlantic and Southern Ocean. First, I will use a physics-guided machine learning methodology to construct hypotheses regarding the balance of drivers in the global ocean. This approach provides a new way to understand the primitive equations. Looking at the Southern Ocean, I will reveal a new unifying framework to understand the gyre circulation, which is critical to the upwelling that is key to climate. Secondly, I will present a groundbreaking methodology to infer subsurface circulation by using a trustworthy neural network that reasons using geophysical fluid dynamics. When used on climate models, this methodology can detect changes in dynamics associated with the Atlantic Meridional Overturning Circulation and reveal differences in model physics that could explain the roots of climate projection uncertainties. Finally, I will discuss the problems of using machine learning as a black box and present solutions. Throughout this talk, I will emphasize the use of data science for discovery, which opens doors to gain new knowledge.