venture capital should be minimal in initial rounds. Nowadays most of the startups know their potential, good or bad. They understand how their competitors are making and how much their startup will be worth. So, I see the point why they are rejecting the VC funds. Most startups nowadays are looking for buy outs or acquisitions on different terms
Managerial positions need more of understanding humans rather than data. Balancing a team, showing a path, leading teamwork are some of the many attributes one should possess to be a manager.
Hello, I have been in your shoes a couple of years ago. I was working as BI engineer in Silicon Valley and realized the potential of ML and Data Science. So I switched gears towards learning algorithms. I learned from logistic all the way to Keras. But as you said, everything is theoretical. As I wasn't working on the practical part of implementing, it has been difficult for me to crack the interviews as most of the questions are related to the project. They ask about your current project in ML and go deep dive in asking why you haven't used a different algorithm, etc etc. I gave interviews and failed in every one of them. But the good thing is that, I have learned through the process and cracked one finally. it is still a mix of BI and ML. but no complains as I don't have handson experience in ML. I suggest you to pick up python, as almost every company asked me to code an algorithm. As you are already an SDE, it shouldn't be of big concern. And the main reason is that, python has vast libraries for data science. Go through the underlying definitions of all ML algorithms. Pick a ML project which your company is working and try to get as much exposure as possible. Trust me, the part-time masters doesn't really matter in cracking a job. It all depends on how well you explained the project, why you implemented a specific algorithm and not the others, challenges in data analysis before implementing ML and of course, coding interview. Good luck.