Environmental Science and Technology Studies (ESTS) examines the “techno-scientific institutions, practices, and knowledge production concerned with the dynamics of natural systems, with social intervention and impacts on the natural world and with the planet’s capacity to sustain life” (Frickel and Arancibia 2021:459). It is a vibrant and broadly interdisciplinary field based in social sciences but spanning into humanities and even some natural sciences. The heart of ESTS are studies of controversy and conflict shaping environmental knowledge, experts, expertise, and regulation – a central nexus of academic and policy science in the current context of ecological, political and economic instability. STS (including ESTS) relies heavily on qualitative tools and methods, mainly derived from anthropology and history. Quantitative data and statistical methods are rarely represented at STS conferences and in the core journals (Leydesdorf et al. 2020, Cambrosio et al. 2020). Newer computational approaches in data science and machine learning – the focus of our workshop – appear even less frequently. We do not mean that STS has ignored data science; quite the opposite. Yet, nearly all this work employs standard qualitative STS tools – ethnography or other types of field-based research, interviews, archival research – to investigate data science knowledge and practice; it does not incorporate data science tools and methods to produce its own knowledge. We believe this methodological disconnect imposes unnecessary limits on the scale, scope, and content of ESTS’s intellectual project and impedes the field’s broader impact on environmental movements, policy and education on both sides of the Atlantic. In this discussion paper, we spell out why we think the disconnect is a problem, summarize past efforts to quantify the study of science and technology, identify some newer lines of STS oriented computational research that show promise as hot spots of innovative inquiry, and pose three key challenges that we believe will move us toward integration of data science within ESTS.