The only advantage of Google Cloud is the TPUs--if you're not running massive machine learning workloads, AWS is almost always the better choice on features, service, and reliability.
The *link between compute and storage* is not even officially a production product:
"Please treat gcsfuse as beta-quality software. Use it for whatever you like, but be aware that bugs may lurk, and that we reserve the right to make small backwards-incompatible changes."
https://github.com/GoogleCloudPlatform/gcsfuse/
If a supposed cloud platform can't even produce a reliable way to access your data, then they have no basis being used in any halfway serious setting.
Software engineering (and most machine learning and data science is still software engineering) requires focus, attention to detail, and memory. The most honest feedback would either be ensure you can get good at one sub-segment of tasks, or move to something else.
Data Science Ability: can you reliably make silver or gold on Kaggle? If not, you just might not be a fit for the area.
If you are able to rank high on Kaggle, or build personal projects using machine learning and deploy them to show off, then you may have a chance--especially if you network in somewhere through alumni and pitch your data science skill.
The other possibility is simply freelancing--I've hired some brilliant Eastern European coders off Upwork. If you can learn one particular set of tasks, then the sheer cost of living arbitrage and your ability to specialize can overcome any slowness that may not be a fit for top companies.
The *link between compute and storage* is not even officially a production product:
"Please treat gcsfuse as beta-quality software. Use it for whatever you like, but be aware that bugs may lurk, and that we reserve the right to make small backwards-incompatible changes." https://github.com/GoogleCloudPlatform/gcsfuse/
If a supposed cloud platform can't even produce a reliable way to access your data, then they have no basis being used in any halfway serious setting.