As someone that was educated in computer science and moved from software engineering to working in the data science world, I find applying to DS positions to be much harder and more painful than CS jobs.
CS is easy: Do you know the languages they want, know some algorithms, are you familiar with the same tools and libraries, how's your experience working with people on big projects?
DS: The field is so broad and so deep, you need know everything and if you don't, be prepared to be shredded. Your interviewer may have a PhD in physics, or a master's in economics, or maybe they're just a math major that did a bootcamp course. Do you know NLP and how to build a pipeline? I feel like I get whiplash when I look at DS job postings they're so all over the place.
The interviews and take-home tasks are all over the place as well. Some of them are pretty reasonable machine learning take-home tasks, but others range from Leetcode stuff to ridiculous 30-hour projects.
Even the potential audience of your work is all over the place. As a software engineer: your audience is the software's users. As a data scientist: Its the software's users, its the developers, its accountants in the finance department, its other researchers at a conference, its the general audience reading the corporate blog, its the marketing department, its whatever C-level executive looking at report. All of whom have vastly different needs and expectations.
CS is easy: Do you know the languages they want, know some algorithms, are you familiar with the same tools and libraries, how's your experience working with people on big projects?
DS: The field is so broad and so deep, you need know everything and if you don't, be prepared to be shredded. Your interviewer may have a PhD in physics, or a master's in economics, or maybe they're just a math major that did a bootcamp course. Do you know NLP and how to build a pipeline? I feel like I get whiplash when I look at DS job postings they're so all over the place.