Data science refers to the set of tools, technologies, and teams that alter the paradigm by which data are collected, managed and analyzed. Data science is, therefore, decidedly broader than ‘machine learning,’ and includes instead the full data lifecycle. Never has the need for effective data science innovation been greater than now when at every turn data-driven discovery is both burdened and invigorated by the growth of data volumes, varieties, veracities, and velocities. This growing scale of science requires dramatic shifts in collaborative research, requiring projects to climb the gradations of collaboration from unidisciplinary, to multi-, inter-, and transdisciplinary (Figure 1, [Hall et al., 2014; NRC, 2015]), and perhaps even to an entirely new level that defies any traditional boundary, or antidisciplinary (https://joi.ito.com/weblog/2014/10/02/antidisciplinar.html). We will discuss the cutting-edge efforts advancing collaborative research in Space Physics and Aeronomy, highlight progress, and synthesize the lessons to provide a vision for future innovation in data science for Heliophysics. We will specifically focus on three trail-blazing initiatives: 1) the NASA Frontier Development Laboratory; 2) the HelioAnalytics group at the Goddard Space Flight Center in cooperation with the NASA Jet Propulsion Laboratory’s Data Science Working Group; and 3) an International Space Sciences Institute project. References: Hall, K.L., Stipelman, B., Vogel, A.L., Huang, G., and Dathe, M. (2014). Enhancing the Ef- fectiveness of Team-based Research: A Dynamic Multi-level Systems Map of Integral Factors in Team Science. Presented at the Fifth Annual Science of Team Science Confer- ence, August, Austin, TX. NRC (National Research Council) (2015). Enhancing the Effectiveness of Team Science. Washington, DC: The National Academies Press. https://doi.org/10.17226/19007.