•1 min read•from Data Science
'Full stack' data science
Our take
The emergence of "full stack" data science roles highlights a significant shift in the industry, where professionals are now expected to manage the entire data lifecycle—from model training to deployment and monitoring. This comprehensive skill set includes knowledge of scalability, computing, and engineering best practices, making it essential for data scientists to evolve beyond traditional boundaries. However, outside of startups or smaller companies, developing these end-to-end skills can be challenging.
I'm noticing more and more roles require end-to-end production skills.
Previously a DS role seemed to involve training a model to solve a problem, or creating a POC, then passing it to engineers to put into production. Now jobs want you to own the whole life cycle from training, to deployment, to monitoring, with knowledge of scalability, compute and engineering best practices.
The problem is outside of start ups or small companies where the role has a large scope, it is difficult to develop these skills. Is this similar to others experience and what do they recommended?
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