More frequent and severe shocks combined with more plentiful data and increasingly powerful predictive algorithms heighten the promise of data science in support of humanitarian and development programming. We advocate for embrace of, and investment in, machine learning methods for poverty and malnutrition targeting, mapping, monitoring, and early warning while also cautioning that distinct tasks require different data inputs and methods. In particular, we highlight the differences between poverty and malnutrition targeting and mapping, identification of those in a state of structural versus stochastic deprivation, and the modeling and data challenges of 1 We thank Nathan Jensen, Varun Kshirsagar, participants and presenters at the AAEA ASSA 2021 Big Data and Near-Real-Time Monitoring of Food Emergencies session for feedback and insights on earlier drafts of this work. Any remaining errors are our own. We thank the United States Agency for International Development for financial support under cooperative agreement # 7200AA18CA00014, "Innovations in Feed the Future Monitoring and Evaluation -Harnessing Big Data and Machine Learning to Feed the Future". The contents are solely the authors' responsibility and do not necessarily reflect the views of USAID or the United States Government.