Transparent data science, profitable AI, and what’s missing from a data science education: Pandata’s Data Scientist in Residence Keith McCormick and Jon Krohn discuss how “insights” can never be the end product of a data science project, how to ensure you have a specific goal at the start of a project that is related to revenue, and why there is so much miscommunication between data scientists and their clients. Exclude the C-suite at your peril! This episode is brought to you by Glean (https://glean.io), the platform for data insights, fast. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • What an Executive Data Scientist in Residence is [05:27] • What A.I. transparency is and how it relates to the field of Explainable A.I. (XAI) [17:34] • How companies can ensure they profit from AI projects [36:47] • Possible organization structures for data science teams to be profitable [1:02:41] • The current gaps in data science education [1:09:58] Additional materials: www.superdatascience.com/655