So long as there is transparency about how pay changes based on location, it is possible to do this fairly. The US Government civilian pay scale is a good example. If you are a GS-12 step 1, your base pay is $66,167, but that goes up with locality pay. A GS-12/1 in New York earns $88,651 but a GS-12/1 in Tucson earns $77,541. It seems like a big difference, but based on cost of living, the GS-12/1 in Tucson probably has a bigger house and fewer expenses. You could argue whether those numbers are right, but at least it's fully transparent. Companies that are moving towards this model should make transparency a priority. Perhaps you can negotiate for base pay and know that it will be adjusted by a certain percentage based on where you decide to live.

Stanza seems like a pretty neat NLP library. I think it may become by one of my go-to tools for text analysis projects. This post is from an afternoon I spent playing around with basic Stanza functionality.

Has anyone ever found a guide of similar format and quality for data science? I would love to find a similar collection of resources for learning data science fundamentals (math, statistics, programming, etc.)