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Now we’re lost in metaphors. A carpenter should definitely be able to build a pergola from a rough paper sketch, and a software engineer should be able to build a machine learning system from an algorithm paper by a data scientist.

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That really depends on what you mean by "machine learning system." For a business, implementing some researched algorithm and essentially using a template? Sure, it's possible. The research that goes into producing the algorithm in the first place? Probably not.

There may be some classes of scientific programming which have simple enough models for a software engineer to implement. Scientific programming that relies on deep understanding of the domain and mathematical models employed to study it don't fit in that category.

Of course it always depends on the context. But my point still stands: a software developer is someone specialising in creating and maintaining code for an application. Any domain knowledge they may need for a particular application doesn’t matter here; their core skill set is software, whereas a scientists core skill set is active research.

Just because some scientific disciplines require extensive programming doesn’t make those scientists good software engineers: Producing an algorithm is different from generating revenue from it.

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