Wrong but useful. Working @Twitter, tweeting about data science and people management.
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At the very least, this article's stage 5 ("Domains") seems to require a big org. It requires a manager, a senior tech-leadish role called "Domain Lead", and a team of more junior DS/DA who rotate between projects at the guidance of their manager and domain lead.
Speaking as a DS manager, this is an appealing model. I've lamented before that it can be hard for two DS with different areas of expertise to collaborate, and a domain lead partially solves for that by being the business-context-sharer-in-chief.https://twitter.com/imightbemary/status/1279837506909859840 …
What's especially cool about this model is how it scales. You spin up a new area with one person in it, and as that area deepens over time and you hire more, they have the advancement path of becoming a domain lead.
The fractal potential of having more domain leads as the area continues to mature is also great, allowing the original domain lead to either have more responsibility over newer leads or to have more peer leads, depending on their wishes and on the needs of the org more generally.
Fundamentally, this "domain leads" concept creates distinct technical and people leadership paths for data science, similar to staff engineers and engineering managers in SWE.
One big difference between DS and SWE is that business context is of similar importance to technical skills. This Domain-based org design solves for this by bundling the skill sets together, and I'm intrigued by its potential.
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