Numerical Relativity as preparation for Industrial Data Science, a personal perspective

Much of the conversation in commercial enterprises these days revolves around industry buzz words such as \textit{Big Data}, \textit{Data Science}, and being \textit{Data Driven}. Beyond the hype surrounding these terms, there is a real, continuously growing movement for organizations to make better use of the data assets they have to inform decisions, strategy, and policy. This push is not unique to the commercial sector; governmental and academic organizations are also embracing such initiatives. The skills required to staff a \textit{Data Science} project typically come from a number of disciplines, ranging from computer science, statistics, modeling and simulation, to information technology, but the emerging wisdom in the community is that the rigor and discipline of a scientific background often makes for the best data scientists. In this talk, I will offer a personal perspective on making the transition from a career in computational physics (specifically Numerical Relativity) to a career in industry, where I have focused on helping organizations make more informed decisions through better access and analysis of data at their disposal. I will identify the skills and training that carry over from a background in physics, discuss the gaps in that preparation, hypothesize as to where this industry is headed, and offer a frank look at a life outside of academia.