The deployment of large-scale data analytics between on-premise and cloud sites, i.e., hybrid clouds, requires careful partitioning of both data and computation to avoid massive networking costs. We present Moirai, a cost-optimization framework that analyzes job accesses and data dependencies and optimizes the placement of both in hybrid clouds. Moirai informs the job scheduler of data location and access predictions, so it can determine where jobs should be executed to minimize data transfer costs. Our optimizer achieves scalability and cost efficiency by exploiting recurring jobs to identify data dependencies and job access characteristics and reduces the search space by excluding data not accessed recently.