If there is one thing that drives the many varied aspects of my career, it is the desire to build large systems with a large number of users that have a big impact. This has led me to focus on distributed computing and networking, machine learning, natural language processing, security and more. Though these skills don't necessarily lend themselves to a single domain of effort, it has given me a number of perspectives that inform my designs of complex systems. Largely, I am therefore an architect and programmer whose focus is a balance between human and machine intelligence. These perspectives are also taken from my career in a variety of startups and academia. I've enjoyed being a part of extremely effective small to medium sized teams building recommender systems, reading level classifiers, smart IoT technologies, and streaming analytics. In academia, I've taught applied data science and machine learning at Georgetown University and completed a PhD at the University of Maryland where I studied planetary-scale data systems. To build great software, you need a great team and as a result, I think knowledge sharing and openness is another critical pillar of my career. I've contributed to many open source projects and am the maintainer of the Yellowbrick Python package - a visual diagnostics tool for machine learning that is affiliated with NumFOCUS. I've presented work at conferences including PyCon and PyData. And finally, I am also an author of two O'Reilly books: Applied Text Analysis with Python and Data Analytics with Hadoop.