If you read my shortish post about staying employable as a data scientist, you might like a longer post by a colleague, Yanir Seroussi. In his post, Yanir lists four possible paths for a data scientist: (1) become an engineer; (2) reinvent the wheel; (3) search for niches; and (4) expand the cutting edge.
To this list, I would also add two other options.
(5) Manage. Managing is not developing, it’s a different profession. However, some developers and data scientists that I know choose this path. I am not a manager myself, so I hope I don’t insult the managers who read these lines, but I think that it is much easier for a good manager to stay good, than for a good developer or data scientist.
(6) Teach. I teach as a part-time job. One reason for teaching is that I sometimes enjoy it. Another reason is that I feel that at some point, I might not be good enough to stay on the cutting edge but still sharp enough to teach the new generations the basics.
Anyhow, read Yanir’s post linked below.
The passage of time makes wizards of us all. Today, any dullard can make bells ring across the ocean by tapping out phone numbers, cause inanimate toys to march by barking an order, or activate remote devices by touching a wireless screen. Thomas Edison couldn’t have managed any of this at his peak—and shortly before […]
Software commodities are eating interesting data science work — Yanir Seroussi
Boris Gorelik 
Both yours and the original post resonated so well with me. Data scientists most embrace engineering skills, and I might not be able to stay at the leading edge too. Thats why I also took up an adjunct teaching position as you, and I am teaching what Yanir recommended -engineering skills for data scientists 🙂
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For years, I kept telling people that I was a “scientist” not an “engineer” . I didn’t want to bother with the “boring engineering tasks”. Thanks to Yanir, I became more engineering-oriented, but mostly in the software engineering realm.
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