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Abstract Biological invasions are a leading cause of rapid ecological change and often present a significant financial burden. As a vibrant discipline, invasion biology has made important strides in identifying, mapping, and beginning to manage invasions, but questions remain surrounding the mechanisms by which invasive species spread and the impacts they bring about. Frequent, multiscalar ecological monitoring such as that provided through the National Ecological Observatory Network (NEON) can be an important tool for addressing some of these questions. We articulate a set of major outstanding questions in invasion biology, consider how NEON data science is positioned to contribute to addressing these questions, and provide suggestions to help equip a growing contingent of NEON data users in solving invasion biology problems. We demonstrate these ideas through four case studies examining the mechanisms of plant invasions in the U.S. Intermountain West. In Case Study I, we evaluate the relationships between native species richness, non‐native species richness, and probability of invasion across scales. In Case Studies II and III, we explore the relationship between environmental factors and non‐native species presence to understand invasion mechanisms. Case Study IV outlines a method for improving the ability to distinguish invasive plants from native vegetation in remotely sensed data by leveraging temporal patterns of phenology. There are many novel elements in the NEON sampling design that make it uniquely poised to shed light on the mechanisms that can help us understand invasibility, prediction, and progression, as well as on the variability, longevity, and interactions of multiple invasive species’ impacts. Thus, knowledge gained through analysis of NEON data is expected to inform sound decision‐making in unique ways for managers of systems experiencing biological invasions.