Load balancing is an important consideration when running data-parallel programs. While traditional techniques trade off the cost of load imbalance with the overhead of mitigating that imbalance, when speculatively parallelizing amorphous data-parallel applications, we must also consider the effects of load balancing decisions on locality and speculation accuracy. We present two data centric load balancing strategies which account for the intricacies of amorphous data-parallel execution. We implement these strategies as schedulers in the Galois system and demonstrate that they outperform traditional load balancing schedulers, as well as a data-centric, non-load-balancing scheduler.