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A solid background in mathematics and statistics is helpful in traditional software engineering but is mandatory for work in machine learning. Says Fred Sadaghiani, CTO of Sift Science, “we are looking primarily for people who have a principled understanding of the statistics, probabilities, and math necessary to grasp the problem. That’s the foundation of this all.” This fundamental knowledge allows machine learning engineers to understand which algorithms best address a problem and how to optimize outcomes.
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