I’ve been a data scientist for the past 10 years, with a background in computer science. In recent years, I’ve found myself spending more time studying, learning, and applying concepts from classical statistics and econometrics, such as synthetic control, multi-level mixed models, experimental design methodologies, and so on. On the other hand, I probably haven’t opened a machine learning book in years.
Do any of you have a similar experience? I think that unless you are working at an ML or computer vision startup, this might be an expected career path. Can you share your experiences? "
I discover that I am kind of on the other route. Despite having a master’s degree in statistics, I now believe that creating dependable and simple-to-maintain software is the most crucial aspect of my work.
I’m therefore learning how to write better code and utilizing technologies like docker, k8s, and some frontend
@Royal My initial goal was to position myself as a code-savvy data scientist. But as time passed, I realized that it was more crucial to concentrate on the business aspect of things rather than the “technology” aspect. While many engineers are familiar with Docker, there aren’t many data scientists who can communicate with the DevOps team and create a statistical model at the C-level.
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