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It's down to the market. There aren't that many beginner or mid-level jobs in the field and there is a big oversupply of candidates. I wouldn't recommend data science or machine learning to people who are changing career direction or need to invest a lot of time/money/effort to catch up with all the theory. Not that it's impossible, just very hard. It's also getting more and more gatekept over time with increasing requirements for advanced degrees.

You're not screwed. If you know any working data scientists // MLEs I would really recommend you get feedback from them on your projects. Ask them to do some mock interviews. Way too often I see bootcamp candidates who do well on technical screens but fall apart during in person interviews because they just did what the instructor told them (use docker, put it on github) rather than understanding why people want to see those things.

The frame of mind that kept me going was focusing on my effort, instead of an outcome. I visualized my effort by tracking job applications. I applied to a lot, so at the end of the day, that metric is what made me feel successful at the end of the day, not necessarily whether some inundated recruiter emailed me back.