I agree with my sibling comment and would add: Max is correct to point out the irony in his anti-thoughtpiece thoughpiece as he falls into the same trap of vag

I agree with my sibling comment and would add: Max is correct to point out the irony in his anti-thoughtpiece thoughpiece as he falls into the same trap of vagueness as those other articles. Specifically, he rails against general “black box” approaches to modeling, then takes a general “black box” approach to the work of operationalizing a model (much harder than building the prototype to begin with!). The discussion of “pulling data” does not match the practical reality, since pulling via BI tools is not scalable and rarely automatable. SQL may cover this insofar as you dump data from SQL to...what, though? A Python session on your laptop? Automating this process allows a data scientist to scale their impact. For more specificity on engineering practices required for data science, I recommend Robert Chang’s series of posts: https://link.medium.com/CG7c7mQdyS https://link.medium.com/CG7c7mQdyS For details on how a data scientist can impact an organization, I recommend this from the FirstMark blog by Jeremy Stanley and Daniel Tunkelang: https://firstround.com/review/doing-data-science-right-your-most-common-questions-answered/ https://firstround.com/review/doing-data-science-right-your-...