The design and implementation of algorithms for increasingly large and complex modern supercomputers requires the definition of data structures and workload distribution mechanisms in a productive and scalable way. In this paper, we propose a PGAS data structure along with a Work-Stealing mechanism for the class of parallel tree-based algorithms that explore unbalanced trees using the depth-first search strategy. The contribution has been implemented and packaged as an open-source module in the Chapel PGAS language. The experimentation of the contribution in a single-node setting using backtracking applied to fine-grained Unbalanced Tree-Search benchmark shows that 68% of the linear speed-up can be achieved. In addition, the scalability of the contribution has been evaluated using the Branch-and-Bound algorithm to solve big instances of the Flowshop Scheduling problem on a large cluster. The reported results reveal that 50% of strong scaling efficiency is achieved using 400 computer nodes (51,200 processing cores).