  Data science for social justice (DS4SJ) is data-scientific work that supports
the liberation of oppressed and marginalized people. By nature, this work lies
at the intersection of technical scholarship and activist practice. We discuss
this growing efforts in DS4SJ within the broad mathematics community. We begin
by defining terms and offering a series of guiding principles for engaging in
critical data science work, providing examples of how these principles play out
in practice. We then highlight the roles that DS4SJ can play in the scholarship
and pedagogy of practicing mathematicians. We focus in particular on the
engagement of early-career mathematicians in DS4SJ, which we illustrate through
a series of four personal vignettes. While the primary aim of DS4SJ is to
achieve impact for marginalized communities, we also argue that engagement with
DS4SJ can benefit the entire mathematical ecosystem, including researchers,
instructors, students, departments, institutes, and professional societies. We
close with reflections on how these various actors can support ongoing efforts
in data science for social justice.
