Building a high-throughput data science machine

Mechanics: forces, gears, axles and dynamics, pulleys. (source: Wellcome on Wikimedia Commons).Scaling is hard. Scaling data science is extra hard. What does it take to run a sophisticated data science organization? What are some of the things that need to be on your mind as you scale to a repeatable, high-throughput data science machine? Erik Andrejko, VP of science at The Climate Corporation, has spent a number of years focused on this problem, building and growing multi-disciplinary data science teams. In this post, he covers what he thinks is critical to continue building world-class teams for his organization. I recently sat down with Andrejko to discuss the practice of data science, the scaling of organizations, and key components and best practices of a data science project. We also talked about…


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