In recent years, serverless computing and, in particular the Function-as-a-Service (Faas) execution model, has proven to be efficient for running parallel computing tasks. However, little attention has been paid to highly-parallel applications with unbalanced and irregular workloads. The main challenge of executing this type of algorithms in the cloud is the difficulty to account for the computing requirements beforehand. This places a burden on scientific users who very often make bad decisions by either overprovisioning resources or inadvertently limiting the parallelism of these algorithms due to resource contention. Our hypothesis is that the elasticity and ease of management of serverless computing can help users avoid such decisions, which may lead to undesirable cost-performance consequences for unbalanced problem spaces. In this work, we show that with a simple serverless executor pool abstraction one can achieve a better cost-performance trade-off than a Spark cluster of static size and large EC2 VMs. To support this conclusion, we evaluate two unbalanced algorithms: The Unbalanced Tree Search (UTS) and the Mandelbrot Set using the Mariani-Silver algorithm. For instance, our serverless implementation of UTS is able to outperform Spark by up to 55% with the same cost. This provides the first concrete evidence that highly-parallel, irregular workloads can be efficiently executed using purely stateless functions with almost zero burden on users-i.e., no need for users to understand non-obvious system-level parameters and optimizations.