One reason to keep an eye on Julia if you are envisioning a long and varied career in the broader computational / data science world is that its not at all clear which will be the leading platform going forward (Python, R, Julia or something else altogether).

This ambivalence might sound absurd on the face of the exponential recent growth of Python (which apparently enticed even some people with serious mojo to get into the act) but take two steps back with me and look at the big (if still hazy) picture:

We are going through a remarkable period where complex algorithmic applications left academia and research labs and diffuse into mainstream society and the economy like never before. This process carries enormous risks and opportunities, which are currently basically... ignored (well, the risk side).

Despite its undeniable strengths and loveability, Python is actually a poster child of the move-fast-and-break-things phase. It is not necessarily best placed for the next phase. The next phase will invariably see a re-examination of all aspects of the stack and qualities that will be prized will be those that eliminate the frictions and risks associated with the large scale deployment of algorithms. The stakes are high, which means there will be plenty of resources seeking to create reliable platforms. The future need not look like the past.

None of the usual suspects ticks all the boxes. In fact we don't even know all the boxes yet. Depends how fast and how seriously models and algorithms get deployed at scale. Python, Julia and R have been propelled forward by circumstances as the main algorithm-centric platforms, and they have each their various warts and blessings but the near and mid-term future will test how well they can deliver on aspects they may have not be designed for.