Data science continues to generate excitement, and yet real-world results can often disappoint business stakeholders. How can we mitigate risk and ensure results match expectations?
Drawing on his experience working as a technical data scientist at the interface between R&D and commercial operations, Martin Goodson offers a personal perspective on the most common failure modes of data science projects. Martin also shares the results of a year-long survey of practicing data scientists, synthesizing this data into a guide to current best practices.
Martin Goodson is the chief scientist and CEO of Evolution AI, where he specializes in large-scale natural language processing. Martin has designed data science products that are in use at companies like Dun & Bradstreet, Time Inc., John Lewis, and Condé Nast. Previously, Martin was a statistician at the University of Oxford, where he conducted research on statistical matching problems for DNA sequences. He runs the largest community of machine learning practitioners in Europe, Machine Learning London, and convenes the CBI/Royal Statistical Society roundtable, AI in Financial Services. Martin’s work has been covered by publications such as the Economist, Quartz, Business Insider, TechCrunch, and others.
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