We present \textit{work assisting}, a novel scheduling strategy for mixing data parallelism (loop parallelism) with task parallelism, where threads share their current data-parallel activity in a shared array to let other threads assist. In contrast to most existing work in this space, our algorithm aims at preserving the structure of data parallelism instead of implementing all parallelism as task parallelism. This enables the use of self-scheduling for data parallelism, as required by certain data-parallel algorithms, and only exploits data parallelism if task parallelism is not sufficient. It provides full flexibility: neither the number of threads for a data-parallel loop nor the distribution over threads need to be fixed before the loop starts. We present benchmarks to demonstrate that our scheduling algorithm, depending on the problem, behaves similar to, or outperforms schedulers based purely on task parallelism.