4 ms·
Increasingly a subskill of other positions, automated, or specialized.
by wendyshu 4y ago
Increasingly a subskill of other positions, automated, or specialized.
- jstx1 4y agoThe specialized part is kind of true to some extent but that's getting bogged down into job titles - data analyst, applied scientist, ML engineer, research scientist - some of these are used more often because "data scientist" is too broad and can mean a bunch of different things. But titles, schmitles - the point is that the skills are still very relevant. And I'm not seeing anything getting automated.
- parpfish 4y agoAgreed. The title data scientist is probably going to go away and be replaced with more specific titles you've listed above. IMO, the emergence of specializations is a good thing because a) it means the data field (and the tools it uses) are maturing and b) you can have a more focused career trajectory instead of feeling the pressure to learn EVERYTHING
- deleted 4y ago[deleted]
- usgroup 4y agoI think this is correct. So a DS does a bunch of stuff. - Some of the stuff becomes diffused and common. Anyone can do it. - Some of the stuff becomes data engineering. - Some of the stuff becomes fitting a model -- anyone can do it. - Some of the stuff ends up subsumed in a cloud providers offering and mostly automated. - Some of the stuff becomes a SaaS (e.g. A/B testing). Death by factorisation. Specialisation is another sort of death, where X doesn't need a "Data scientist" any more, they need a "X data scientist". A bit like how "scientific computing" is just programming, except you wouldn't hire "just" programmers to do it -- you hire programmers who specialise in scientific computing.