Interdisciplinary collaborative research is a key component of data science and for some of us, plays an important part of our roles as statisticians. It is not unusual that we become accustomed to vertical thinking whereby we use existing tools and methods in our own specialty to problem solve, losing sight of the larger interdisciplinary context of data science, and the context of the scientific challenge. The Government of Canada - Science and Technology branch has identified several key priority research challenge topics that involve cross-disciplinary work. Although statistical tools and analytics are identified in these research challenge priority areas, additionally, the development of fundamental transformative and enabling technological tools specifically for statistical methods and analytics to support research and societal advancement is also seen as a priority. This talk shares insights about the challenges and opportunities for statistics in interdisciplinary research. Specifically, monitoring viral signals in wastewater and assessing forest fire risk are given as complex, case studies that use a collaborative and interdisciplinary approach to solve difficult problems. This approach will demonstrate the significant benefits for not only optimizing research impact but for training students to become horizontal problem solvers across a wide range of research methods which will benefit them in navigating complex problems and in the development of appropriate tools for their analysis.