111–120 of 415 posts
Calling it Data Science was a tell. Have you noticed how non-scientific things add "science" to the name to make it sound like it has scientific rigor? Data Science, Political Science, Social Science, Scientology Compare to Physics, Biology, Math.
Earlier quoted context omitted.
This is the defining pain point for data science, in my experience. There’s no simple ground truth to test competence against. If someone tells you that the data says their work is good, the only real way to know if they’re right or wrong is to look at what the data says yourself. If 99% of the work is building and 1% is checking something like latency, then you’re likely to have more than one set of eyeballs on that…
That's the same for many tech jobs. Competence is often only a local thing, subject to politics, reputation, and appearances. There's also no ground truth because the ground changes so fast. No one knows if the technologies mentioned in the OP will be popular 5-10 years from now.
The output of Data Science is harder for non-specialists to evaluate.
// it was often personally unfulfilling (e.g. tuning a parameter to make the business extra money). He lost me here. Something I've always loved about being an engineer (and now in product) is that something small we do/tweak can have big impact. If you tuned a parameter and that actually had tangible impact on the business, that's like the best case scenario and should be celebrated (vs doing some cool rocket scienc…
If I work my ass off and my model recommends a few extra units, I don't see another dime, so where's the motivation?
> Nobody knew or even cared what the difference was between good and bad data science work. Meaning you could absolutely suck at your job or be incredible at it and you’d get nearly the same regards in either case. In my experience it's even a little bit worse than that. Approaches that are wrong from a statistics point of view are more likely to generate impressive seeming results. But the flaws are often subtle. A…
I've seen this a LOT in my professional group. Many people (who often have PhDs!!) I interview for data science positions seem to know absolutely nothing about the algorithms they use professionally, or how to optimize them, or why they are a good fit for their use case, etc etc etc. I usually see through LinkedIn that these same people are now in impressive-sounding positions at other companies. I had one candidate…
Oh yes, good old marketing.
Along with buying off "Industry Awards" – hey, we're objectively the "Best cybersecurity company of 2022!" With a matching "platinum/gold badge" to go on our website! Or buying a place in the "10 Best Products for X" and "Independent X-vs-Y Comparison", another classic.
Because it works. Are your customers not sophisticated? Are they unable (or unwilling) to follow up on defects and outright lies? Or reality simply doesn't matter all that much to them? Humans LOVE a good story more than reality, after all.
Then your contribution as an engineer to your company's success, and hence its longevity and your job security, is strictly inferior to that of marketing. Not everything is the work of evil marketers – a lot of the supplied BS is in response to an existing demand for BS.
Earlier quoted context omitted.
To counter your professor opinion. The amount of extra time available as a student that I had to pursue things of interest was in the negative. All academic time was spent getting course content accomplished. I am a naturally curious individual but time limitations prevent further exploration in most circumstances. Additionally there is a relevancy factor weighed on top of it. If something looks curious I have to pre…
Well good luck then, in my experience the most free time I've ever had in my life was during college. I squandered massive amounts of that time doing things completely unrelated to education, and I definitely don't regret doing that. College isn't just about book learning after all. But still, BY FAR, college is the time of my life when I had the most free time to do whatever I wanted.
Since then workload has been intense of course but never comparable. I've had much more time to be able to explore personal interests since college.
As someone who also wants to move away from data science, data engineering is the last thing I would want to do. I think DE comes with many of the same problems and it's also a very ill-defined career track; I wouldn't recommend it to anyone. ML engineer or backend developer seem like much more appealing job profiles.
I love a lot of it, but there's still plenty of bullshit to deal with. Just in the technical side, dealing with Python is a perpetual gong show, and most of my team's work seems to revolve around configuration of secrets and K8s.
I'm fortunate to be the guy that nerds out about performant code, so when something inevitably turns out to be a perf bottleneck, I can turn back into a regular old software engineer who trades in big data. Which I think is a better title/charge than data engineer, anyway.
I've talked with plenty of ML engineers, and they seem to immensely enjoy what they do. It seems that the periphery of data engineering is great; the core of it, not so much.
> Nobody knew or even cared what the difference was between good and bad data science work. Meaning you could absolutely suck at your job or be incredible at it and you’d get nearly the same regards in either case. In my experience it's even a little bit worse than that. Approaches that are wrong from a statistics point of view are more likely to generate impressive seeming results. But the flaws are often subtle. A…
I become wary any time someone utters the phrase, "show me the data" or any variation there of. There is a specific type of leader who thinks that within the data lurks a magical solution just waiting to be discovered. There is also the leader who uses data as a trump card to win arguments and these folks are perhaps even worse. This is not new. The origination of the phrase, "lies, damned lies, and statistics," can…
I can relate to this so much. I've worked in multiple projects with great people who shifted away from solving the problem at hand, to instead construct some sort of generic problem-solving platform. In one project this actually happened twice: after refactoring the beef out of our SpecificProblemSolvingService into GenericProblemSolvingService, the generic problem-solving platform was then rewritten with one extra level of abstraction, so it could run any models designed to solve any task. As far as I know, neither service was ever used to solve any other problem except the SpecificProblem that we were solving the first place.
But it's fun writing platforms, I guess?
Unfortunately it seemed pretty clear from the start that this is what data science would turn into. Data science effectively rebranded statistics but removed the requirement of deep statistical knowledge to allow people to get by with a cursory understanding of how to get some python library to spit out a result. For research and analysis data scientists must have a strong understanding of underlying statistical theo…
I dont know anything about Data Science but as a bystander with a mathematical background thats what I assumed was going on so its kindof interesting to see it spelt out like that. Like you've put words to a preconception that I didnt even know I had.
Earlier quoted context omitted.
I've seen this a LOT in my professional group. Many people (who often have PhDs!!) I interview for data science positions seem to know absolutely nothing about the algorithms they use professionally, or how to optimize them, or why they are a good fit for their use case, etc etc etc. I usually see through LinkedIn that these same people are now in impressive-sounding positions at other companies. I had one candidate…
> clients will come back to us saying "XYZ says they can get better performance" Oh yes, good old marketing. Along with buying off "Industry Awards" – hey, we're objectively the "Best cybersecurity company of 2022!" With a matching "platinum/gold badge" to go on our website! Or buying a place in the "10 Best Products for X" and "Independent X-vs-Y Comparison", another classic. Because it works. Are your customers not…
You would probably be depressed if you knew who our customers were, and how technologically unsophisticated they are.