The moment Julia shines is when your workloads can't be phrased by stringing together the limited set of vectorised verbs that python / r libraries give you: this is anything stateful and loopy like reinforcement learning, systematic trading, monte carlo simulations etc. It's also useful if you really care about performance and are doing "vanilla" computations at a truly large scale. If you want to avoid copying memory (i.e. doing vectorised operations), or want to tightly optimise / fused some numerical operations, it's great.

The other issue with python / r wrapping c++ libraries is that different libraries will generally not play well together (without coming out into python / r space, and doing a lot of copying / allocation). This tends to encourage large monolithic c/++ codebases like numpy and pandas, that are pretty impenetrable and difficult to extend / modify.