Ain’t no thing as a ‘Full Stack Data Scientist’

A Full-stack Data Scientist is not a 10x Data scientist but a 1/10th Data Scientist.
Data science does involve coding but it shouldn’t be viewed through the prism of software programming. Words such as ‘Full Stack’ loaned from the software world should not be applied to Data Science.
Why Full-stack does not apply to Data Science
‘Full Stack’ in the software world means a person can do both front end and backend development. Often in both cases, the objective, design, and functionality is known. The ‘what to be done’ is already known and ‘how to do it (write code)’ is also known.
In comparison, in a typical data science project, ‘what to be done’ is often known in the form of a business problem/objective. What is not known is ‘how to do it’. This makes most of the data science projects ‘Experimental’ in nature.
A full-stack data scientist is expected to do data engineering, devise or think of relevant algorithm to apply (R&D), but the algorithm into production, communicate the results with stakeholders (via good data visualization) and to top it all know the domain really well.
The above definition implies that a full-stack data scientist skillset has to spread really wide but in doing so, one also spreads oneself very thin. We all have limited time, there is always a trade-off between what you can specialize in and what you can’t specialize in.
Those who think a data scientist can be full-stack unfortunately don’t understand how vast a subfield in data science can be !!
Even within the Machine learning algorithm know-how, one can’t claim to be full-stack (i.e. know probability, know statistics, know linear algebra, know topology, know calculus, know geometry, etc thoroughly). Just to master these itself will probably take a lifetime.
Ain’t no thing as a ‘Full Stack Data Scientist’.

