Single-cell sequencing technologies have revolutionized our understanding of the heterogeneity and dynamics of cells and tissues. However, single-cell data analysis faces challenges such as high dimensionality, sparsity, noise, and limited ground truth. In this 3rd installment in the Open Problems in Single-Cell Analysis competitions at NeurIPS, we challenge competitors to develop algorithms capable of predicting single-cell perturbation response across experimental conditions and cell types. We will provide a new benchmark dataset of human peripheral blood cells under chemical perturbations, which simulate drug discovery experiments. The objective is to develop methods that can generalize to unseen perturbations and cell types to enable scientists to overcome the practical and economic limitations of single-cell perturbation studies. The goal of this competition is to leverage advances in representation learning (in particular, self-supervised, multi-view, and transfer learning) to unlock new capabilities bridging data science, machine learning, and computational biology. We hope this effort will continue to foster collaboration between the computational biology and machine learning communities to advance the development of algorithms for biomedical data.