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It's a price data scientists have to pay in order to work in rapidly evolving business and solution spaces. Someone within the local organization has to experie

It's a price data scientists have to pay in order to work in rapidly evolving business and solution spaces. Someone within the local organization has to experience all these tools before being able to reach similar conclusions. Many organizations are still struggling to get the data science infrastructure in place so they look for full-stack people to help get the ball rolling and start making progress on some initial set of prioritized business problems. A few organizations are further along on that journey enabling their data scientists to focus on things other than process and tooling. Full-stack will be in demand until the solution space stabilizes and the bulk of organizations catch up.

hobofromabroad 5y ago
That might be true for startups. But larger business organizations are far better of creating a specific heterogeneous team with data scientist, data engineer and ops in one. At least starting out. That way, there is inherent knowledge transfer. You are not artificially limiting your hiring pool and can actually get some T shaped folks being experts in a certain domain. Later on you can then build more specific teams or even more cross functional ones. Of course, if you only want feel the waters and check if DS use cases are viable at all, consider getting a (few) freelancers and but a somewhat technically inclined person in charge. If that's a success use it to get funding for a proper team.