As data science increasingly shapes educational programmes, research agendas, and societal narratives, its practices have come under scrutiny for reinforcing historical inequalities, perpetuating biases, and neglecting critical engagement with issues of power, capital, and representation. Drawing upon critical social science theories including decoloniality, intersectionality, radical transdisciplinarity, and reflexivity, this paper narratively explores the limitations of conventional data science methods and pedagogy, advocating instead for a critical paradigm shift aimed at reclaiming data science for just geographies. We highlight the necessity for an approach that recognises data science as inherently subjective, deeply embedded in social and political contexts, and fundamentally shaped by historical legacies of colonialism and exclusion. By situating our experiences within universities in Western Europe, we illustrate how education and research can inadvertently perpetuate harmful structures when failing to critically engage with the positionalities and power dynamics inherent to data practices. Responding to these broader societal challenges, we propose a practical, iterative framework for critical data science that has emerged from our teaching methods and research experiences. This framework invites researchers and educators to continually reflect upon inclusivity, inequality, participation, power, and positionality throughout each stage of the data science process. Ultimately, our aim is to empower a generation of data scientists capable of interrogating dominant narratives, embracing diverse perspectives, and collaboratively working towards more equitable, just, and caring futures for all.