Great article for anyone who's interested in data science/analytics org design. It talks about the evolution from chaos to centralized to embedded to pods to domains, and all the task/career management woes associated with each stagehttps://medium.com/snaptravel/how-should-our-company-structure-our-data-team-e71f6846024d …
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I've been through several of these phases myself, and they happened in a similar order during similar years. I can't tell if the team structures are more a function of org size or of what was conventional wisdom of org design at the time
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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.
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Katie Bauer! Retweeted Katie Bauer!
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 …
Katie Bauer! added,
Katie Bauer! @imightbemaryIt also makes it harder for DSes to collaborate. A team of two horses can pull more weight together than two horses working alone. Engineering teams, which are inherently more modular, function like teams of horses, and Type A DS teams function like individual horses.Show this thread1 reply 0 retweets 1 likeShow this thread -
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.
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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.
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Fundamentally, this "domain leads" concept creates distinct technical and people leadership paths for data science, similar to staff engineers and engineering managers in SWE.
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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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