The working definition of a Data Scientist seems to be in the current overheated environment:
doing whatever it takes to get the job done in a digital #tech domain that we have long neglected but which is now coming back to haunt us!
That is nice urgency while it lasts, but it is not a serious job description for the future.
You will always find entrepreneurial institutions to offer degrees and certifications on the latest trending hashtag. This does not mean that data scientist is a professional label that will last.
Long term stable professions normally plug into the legal backbone of society (think of guilds and associations). In the future, a number of distinct professions may evolve out of the current #datascience soup. What will those be? Remains to be seen, of-course, but the following offers a mental model to guide searching for plausible answers:
Data, quantitative tools and associated knowledge are the assets of data science. The suggestion is to focus instead on the key liabilities emerging when the management of digital information becomes so prevalent:
- Data privacy
- Data provenance (and quality)
- Model performance (model risk)
- Model acceptance by all stakeholders
It is expertise in the management of such liabilities that makes professions indispensable, and these may vary significantly depending on the domain.
Those who will alleviate these emerging pain points of our digital society will inherit the kingdom!