Derivative-free optimization (DFO) methods are an important class of optimization routines for many problems in data science, such as hyperparameter optimization and adversarial attacks for neural networks. However, in model-based DFO methods, the computational cost of constructing local models and Lagrange polynomials can be high. As a result, these algorithms are not as suitable for large-scale problems as derivative-based methods. In this talk, I will introduce a derivative-free method based on exploration of random subspaces, suitable for nonlinear least-squares problems. This method has a substantially reduced computational cost (in terms of linear algebra), while still making progress using few objective evaluations. I will also discuss how this approach may be extended to DFO for general nonlinear optimization problems.