False predictions and drowning in data

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Google has always been a place where statisticians are hired to interview potential employees for jobs in the field.
The job of a statistician has changed over the years, as the problems that they are tasked with have shifted.
The current problems that statisticians are faced with are made even harder by the large amount of data that is available.

Angel investor and data science consultant Josh Wills sits down with Jon Krohn to discuss his former roles (Google, Slack, and Cloudera) and the essential skills for engineering scalable machine learning projects. This episode is brought to you by Pathway, the reactive data processing framework (www.pathway.com/?from=superdatascience), and by epic LinkedIn Learning instructor Keith McCormick(https://linkedin.com/learning/instructors/keith-mccormick). Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • Josh's 'Data Engineering for Machine Learning' course [06:50] • Contextual bandits [10:52] • Data quality and monitoring [16:45] • The “infinite loop of sadness” in data product development [25:12] • Josh’s definition of a data scientist [30:02] • Josh's role at WeaveGrid [37:36] • Management-Track vs Independent Contributor [48:47] • Josh's work on the Covid pandemic [1:06:46] • Josh’s favorite tech stack [1:11:13] Additional materials: www.superdatascience.com/665