Yes, Data Scientists should develop their software engineering skills. Let me react to a LinkedIn post by Neil Leiser.
But Data Scientists can’t do it alone, or by themselves. Read on.
I see that software engineering, IT architecture is a touchy subject amongst even the best data scientists, usually because they came from other knowledge domains as economy, statistics, pure math, physics, biology etc. This is a normal evolution. Data Science demands a wide broad skill set, sometimes too wide and too broad. Data Scientists need to handle Docker and HTTP APIs along with outliers, RMSE, ROC curves and Gaussian distributions. Go figure…
ML engineers — usually folks that have more software engineering background — should help here.
But the most important thing ➔ it is the mission of the CDO, tech lead or CTO with strategic vision to clearly detect these gaps and design a roadmap to handle them, not just with conventional training but also encouraging mixed squads whose members will exchange skills and knowledge, leveraging multi-disciplinar environments where everybody grows together.