Solution of large-scale nonlinear stochastic mechanics problems such as plasticity is generally very expensive. In this work, a domain decomposition based scalable method is proposed for solving such problems. The mechanics problem and random fields are discretized using finite element (FE) bases and Karhunen-Loève expansion, respectively. The FE mesh is partitioned that offers (i) both spatial and stochastic dimensionality reduction and (ii) an inherent framework for parallelization. Then a stochastic collocation based surrogate model is built for each subdomain wherein at each collocation point a deterministic nonlinear problem is solved. The deterministic nonlinear problem is solved using Newton-Raphson method. The linear system of equations involving the Jacobian is solved using the dual-primal version of the FE tearing and interconnecting (FETI-DP) method, due to its demonstrated scalability for deterministic problems. Stochastic collocation and FETI-DP are inherently and independently parallelizable. Finally, at the post-processing stage, a statistical sampling from the surrogate model is performed by preserving the structure of the input random field. The proposed method is numerically tested for p-Laplace and plain-strain plasticity problems, and found to be computationally efficient and accurate. In parallel implementation, the method showed good scalability.