>Meaning you could absolutely suck at your job or be incredible at it and you’d get nearly the same regards in either case.
One of the things I don't like about statements like this said in a Data Science context, is that they are true outside of Data Science as well. Executives make big decisions, managers make smaller decisions, nobody can evaluate how good/bad they really were for months or years. Engineers build something amazing, or build a house of cards, nobody cares as long as the money people are happy, even if the business use case turns out to be wrong in the long run.
>With a short-term focus they also won't really care, because they can still put these results in marketing materials and impress most outsiders as well.
Forget Data Science, you see this in KPIs as well. Say a crappy metric has to be moved by Q2 next year and people will destroy the company to move it.
I feel like Data Science is just one of those areas where you are exposed to a wider range of people and get to feel the full crapola of the insanity of working in a corporation. For lots of roles (e.g. Engineering) you get to hide in a hole behind layers of people and not see some of this insanity.
I attended a R1 university, and it was up or out for professors. I think they were encouraged to either get tenure or move to another school where they would get tenure. It's no different that a private sector jobs where you are expected to get promoted to a certain point or you need to move on.
I wouldn't call them risk averse. You get paid peanuts and treated like crap for years in the hope that you'll find a research path that generates funding. It's a lot less risky to get a private sector job and make money.
As far as Elon Musk versus professors, it's a different type of risk. I knew somebody who sacrificed a lot of potential money for years of his life so he could try an contribute to research for a specific medical condition. He was brilliant and I'm sure he could have made 4X the salary in a tech job.
I've got a PhD. Personally, I'd say "unrealized" instead of "unattainable." Unattainable makes it seem like a dissertation topic is always some magical effort.
Lots of PhD work is a investigating some mundane area that somebody else hasn't (due to lack of time or interest) investigated yet. I wonder how many dissertations come from a random comment by a professor saying like "Well here is some minor tangential topic I/somebody should look into."
How would you define medium sized? How many employees and/or how much revenue? I'm asking because I feel like a lot of people have different ideas of what they consider "medium." I used to think it was 2000-5000 employees, but I've come to realize that's a large company.
>Working from home for the past 3 months taught me that a lot of my constant tiredness and apathy came from the constant noise you get in an open office.
I can completely agree with this. I've had a lot of jobs, and I had one in a big (but crammed) open office space where I sat next to people in roles (e.g. HR) that required them to talk all day. The constant noise, movement, and loss of privacy just sucked out all my energy. Going home after work did nothing to refresh me because it was like recharging a battery that was at -80% to -60%. Ironically, it was the highest paying job I've ever had and also the one where I got the least accomplished in the time I was there due to that office.
Mind you, this was in NYC. Going outside into the chaos of NYC noise and traffic felt relaxing compared to that office.
>As a positive, if I were 20 years older I'd probably have started working in cubicles, which have all the negatives of an open office but without any natural sunlight.
I don't think cubicles have all the negatives of open offices. At one job I had a small cubicles where the dividers were about 5 feet high. Despite all the cubicles around me being people from tech support (who were on the phone all day), I just had a much greater sense of isolation and privacy. I was very productive in that environment. The tech support people were enterprise support people though, so their conversations had lots of pauses while somebody dug through log files, made config changes, etc. Might have been very different if they were support regular consumers.
I've seen cubicles where the dividers are chest/shoulder height. Those cubicles might as well be an open office, but at least you have some allocation of personal space on your desk.
>This is often not a good sign, i.e. if the code is so messy no one can understand it. But if it's so technically challenging only him can understand, then that's a great position to be in!
What he built required specialized knowledge of a GUI driven product that had a very convoluted C# SDK for developing custom add-ons. So it was less about messy code and more about there being very few people who could understand all 4 aspects (The product, C#, the SDK, and how the SDK related to the GUI).
>I guess the tldr of your story is to stay away from these big companies and to work directly for your clients?
Yes and No. Sometimes to get paid well as a contractor you need to find other ways to get gigs than finding standard contract recruitment that comes through LinkedIn. In the case of the person I knew, he had basically created an add-on product and was charging hourly for services to maintain and add features.
One of the things I don't like about statements like this said in a Data Science context, is that they are true outside of Data Science as well. Executives make big decisions, managers make smaller decisions, nobody can evaluate how good/bad they really were for months or years. Engineers build something amazing, or build a house of cards, nobody cares as long as the money people are happy, even if the business use case turns out to be wrong in the long run.
>With a short-term focus they also won't really care, because they can still put these results in marketing materials and impress most outsiders as well.
Forget Data Science, you see this in KPIs as well. Say a crappy metric has to be moved by Q2 next year and people will destroy the company to move it.
I feel like Data Science is just one of those areas where you are exposed to a wider range of people and get to feel the full crapola of the insanity of working in a corporation. For lots of roles (e.g. Engineering) you get to hide in a hole behind layers of people and not see some of this insanity.