[motivic]

This is a bit off-topic but one thing I'm curious is how the author manages to interview with these companies while holding down a full-time job and keep up-to-date with the latest developments. Interview for a data science position is typically a drawn-out process, with multiple rounds of interviews and possibly take-home projects. I found them to be very time and energy consuming.

To share my story, I also had a difficult time transitioning into a data scientist role after leaving academia (pure mathematics), and I always thought the root cause was my lack of experience and competency. So instead of keeping on applying, I spent over a year just to sharpen up my skills. It paid off in the end.

How can one develop his/her skills and cultivate expertise if one is job-shopping all the time (possibly aimlessly)?

[eanzenberg]

I think being a good DS requires focused learning. For example, when I started out, I didn't know much about neural networks and their different architectures. I tend to find time outside work to go over papers, thesis, or watch lectures on youtube to keep myself up to date so that now I can describe cnn and rnns to tech and non-tech people.

[motivic]

A better but more difficult approach is to distinguish oneself and have the companies go after you.