Two Frustrations With the Data Science Industry

NathanBrixiusOn May 13, 2017 Nathan Brixius described his frustrations with data science industry (such as overclassification and overreliance on credentials) starting with this rant:

I don’t give a shit if you call yourself a data scientist, an analyst, a machine learning practitioner, an operations research specialist, a data engineer, a modeler, a statistician, a code poet, or a squirrel. I don’t care if you have a PhD, if you went to MIT or a community college, if you were born on a farm or in a city, or if Andrew Ng DMs you for tips. I want to know what you can do, if you can share, if you can learn, if you can listen, and if you can stand for what is right even if it’s unpopular. If we’re good there, the rest we can figure out together.

Nathan has serious reasons to speak like that because: “As opposed to academic or research positions, my own work in industry has been focused on the practical use of data science to address business problems. More often than not, I’ve worked as part of a team to get the job done. What matters for people like me is whether problems actually get solved, in a reasonable amount of time with a reasonable amount of expense.”

If you develop real-world operational decision models, Nathan’s frustrations and comments to his “small rant” force you to think about the current state of the “decision modeling industry” as well.

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