As a gay man, I will begrudgingly volunteer as a sacrificial genital checker. I will sit at the entrance to the men's restroom, like a bouncer of a club, and the men that are concerned about who is in which bathroom can show me their penis prior to gaining entry to the restroom. That way, we can make sure restrooms are safe for all.
I'm struggling with this at my current job: nobody communicates about anything in asynchronous channels, doesn't want any form of daily synchronous meeting (e.g., standups), and won't agree to ad hoc meetings outside of our 1 hour once per week meeting. So lots of decisions and work get done in vacuums, which cause errors in various systems that would have been easily caught and addressed if someone just said "hey, I'm changing this column name from X to Y". So, just to say that the counterfactual here isn't "no stress because no notifications"-- it can be more stress from failed coordination.
There are in betweens here, with the major one being threads in slack. Everyone gets notified about a single message at the start of the thread, but does not get notified for any subsequent discussion. Any interested party can read more and participate as needed. For someone like me (a leader on paper but not really in practice), I'd read all the message and look for dependency or similar problems, but for others they may not need to.
I like the way you think... Wanna join my startup? I'm looking for a technical cofounder. A ninja. A 100x engineer. I can offer 36k/yr and 0.1%. I'm a fast-paced ideas guy, and I need you to be on your implementation game. I'm in touch with some top notch investors-- just waiting for them to respond to my emails, and then we're going to be the next unicorn. This comp package is going to be your ticket to early retirement-- just think, Apple has a 3.5bn market cap, so if you have 0.1% of that you'd be sitting on millions.
(I think I've been in the SF tech scene too long-- I've literally had every one of those things pitched to me before)
> In general I’m frustrated that patients can’t just order the exact tests they want.
See jasonhealth.com (for Quest) or ownyourlabs.com (for Labcorp). The main issue is that you can't bill insurance without a provider's order, but a lot of tests are cheaper when not billed through insurance.
ACH often refers to filtration and not ventilation; that is, recycled air counts for ACH calculations but won't decrease CO2 because that's not scrubbed by the filters. So it's possible you can have high CO2 levels but also no virus or particulate matter in the air; this is why some places will report PM2.5 as well as CO2, as a proxy for filtration. To make it more complicated, you can also use UV-C to inactivate pathogens, which may not decrease PM2.5 and won't decrease CO2.
I'm not saying this is the case necessarily for planes, but I'm just trying to provide context for how proxy measures of air quality may not tell the full story.
> to imply that dissenting opinions aren't supported by science to be pretty offensive
Unless I missed something, you have yet to share the science that supports your view. I'm not bowing out of discussing this because we have different opinions, I'm bowing out because I shared a study (and could provide more) and you responded by shifting goal posts and standing by your claim, not by responding in kind with similar studies or different interpretations of the data. There's not much discussion to be had if we're not working from a shared understanding of data and facts, and those data and facts aren't driving our opinions and beliefs. Anyway, best of luck out there!
Multiple citations needed here, but your response suggests science isn't driving your beliefs here, so I'll just point out one thing and then will leave this thread:
> More often than not that's what people mean when they're saying that politicians have given up on fighting Covid.
I know a lot of people in public health and disability spaces, and every person I know that talks about the failure of public health around the covid pandemic is referring to the dismantling of surveillance (e.g., testing), the lack of investment in next generation vaccines and treatments, the failure to upgrade ventilation and filtration, and removing mask mandates in targeted places (like emergency rooms). I haven't heard anybody in the US discuss lockdowns in years. I haven't even heard people talk about broad (i.e., outside healthcare) mask mandates in over a year. You need to get "mitigations = lockdowns" out of your head, that's not what people are "implying" when they discuss fighting covid.
You moved the goal posts. Your claim was that "lockdowns" did little or nothing, and I provided a study that shows that simply isn't true. You're now disagreeing (without any evidence) but have shifted to arguing they have side effects that you find too severe.
That's a completely different argument, and one that really can't be objectively measured (e.g., how do you value a life saved?). But lockdowns were effective at saving lives in 2020, and since we knew nothing about SAR-CoV-2 and had limited treatment options there was basically no other option. Nobody (other than you) has mentioned reinstating lockdowns.
> They just implemented authoritarian measures such as lockdowns that had little to no effect, but simply served those politicians' own self-serving purposes
Had all states had restrictions/"lockdowns" in 2020-2022 that were as strict as the strictest state's mitigation measures, we would have saved ~360k more lives[1].
I volunteered as a crisis counselor: was trained in crisis counseling, and then thrown on phone lines for hundreds of hours. I use those skills every day in work and my personal life.
The three biggest parts that I learned and got good at, which have come in really handy are: reflective listening, non-directive communication, and not asking "why" questions.
Reflective listening is repeating back what the person is saying to them. It's definitely more art than science, since if you do it too overtly it just feels cheesy and not genuine. But done subtly and well, people open up in really noticeable ways.
Non-directive communication means not telling people what to do. That doesn't mean you don't have opinions about the situation or that you don't push them towards a decision... it just means that your role is to be a sounding board. If someone comes to you saying "My partner cheated on me", the response many people have is "dump them!" -- but you're not living their life, you don't know all the complexities of the situation even if this is your best friend. So, instead of "dump them", a series of questions can often help someone through the situation and leave everyone feeling more seen and heard ("What happened? How are you feeling about that? What do you think this means for your relationship? What would have to happen for you to trust them again? How would you feel staying with them? breaking up with them?" etc)
Not asking why questions is difficult, but once you get the hang of it it's really easy. "Why" questions are almost impossible to say without there being some tone or judgement. "Why did you do that? Why did that thing break? Why didn't you tell me that?" You can often rephrase the question to a more neutrally worded question (sometimes they also sound judgmental, so tone and context matter a lot): "What were you hoping would happen? How did you see this playing out? What do you think led to the system breaking? What can I do differently to allow you to feel comfortable sharing?"
Ultimately it all takes practice, and fundamentally you need to have the time and space to be a good listener. If you only have 2 minutes between meetings, then you're probably not going to have the capacity to listen to your friend talk about their divorce or health scare. But ultimately people like talking about themselves while feeling safe-- so anything you can do to create and encourage that environment will cause people to feel like you're a good listener.
Nice! Now ban NDAs when it pertains to claims of illegal activity including discrimination. I know there are some really fascinating stories floating around out there...
There's really two parts to worry about: getting an interview, and then passing the interview.
The getting the interview part is difficult: some places will more liberally interview candidates, and others more heavily screen them. The three things that you can change to improve your odds here are tailoring your resume to use more words and phrases from the job description (trying to game any AI or human resume screener), networking to try to bypass the screening stage altogether, and casting as wide of a net as possible. Networking can be anything from having a social media presence, going to various forms of dev events, talking to friends about open roles they've heard about, or even cold emailing people you're interested in (although, if you do this, you'll probably have more luck asking to zoom/coffee for career advice than asking if they have a job available for you). And then, regarding the role you're targeting, I moved into increasingly technical roles starting as a data analyst-- I know not everyone would agree with this approach, but it worked well for me. I was a really technical analyst, who became a data scientist, who worked up the ranks, and then started moving between DE/MLE/DS roles. But this was also back in the days when "data scientist" was a new term, and before it got so watered down-- so maybe with title inflation, my original "data analyst" jobs might be "data scientist" jobs today? Anyway, my point is that I think it's easier to slowly slide in to your ideal role than it is to try to hop directly into it.
The passing the interview part ends up being so much about how you communicate and frame work that you've done. It sucks because this ends up inadvertently screening out really smart/good people that struggle with this kind of thinking (and screening in people who are good at talking but suck at doing e.g., many MBAs). But once you're talking to a live person, I think emphasizing how your degrees (both grad and ugrad) have really prepared you for exactly the role in front of you. You can also often take non-technical experience as evidence of certain components of the technical job requirements. Like, I worked in a restaurant when I was a teenager, and you better believe that prepared me to deal with many concurrent demands from many different sources, and required me to think on my feet about the priority/order of operations. So, when I was earlier in my career, I got really good at answering questions along the lines of "you know, I haven't done this work in a single role, but I have experience doing everything you're asking for over multiple roles..."
But it sounds like a lot of the issue you're running into is just getting in the door to begin with? Unfortunately, I think so much of it comes down to luck-- just keep applying to as large of a variety of jobs as possible, and network as much as you can.
I'm obviously painting in broad strokes, and for sure "data science" as a degree has become more mainstream over the years. Personally, I tend to interview everyone I can fit into my schedule that's been handed to me by a recruiter. And I have hired grad school dropouts, bootcamp grads, no grad school, graduates of DS programs. There's so much variation across with all of these things that it's difficult to make a highly sensitive and highly specific rule based off education alone.
I'm just saying that I seem to have more luck with people coming out of those traditional programs. But, also, as you add more jobs to your resume, the specifics of your education matters less and less.
it was a very toxic environment. The DS team existed for years within the clinical org, and then the CTO decided they needed to do more ML so created an MLE team. Originally it was pitched as an engineering team to create the pipelines to enable more ML by whomever (including DS), but the team members were more interested in solving organizational problems... but generally weren't equipped (connections, time, skills, whatever) to actually do that.
So, some of the "working on the same problems" was intentional-- it would require effort from both teams. But the dividing line was nebulous. The DS team would have preferred to do all of the stakeholder work through building a model, and then hand a pickled model to the ML team to implement in production. The ML team would have preferred to have the DS team scope the problem and hand off to them to do anything involving any form of modeling. It was a total mess.
But I have never worked at an organization where this has gone well, so I don't think it was an issue specific to that org. If you're involved in data things, you want to do interesting work and there's only so much interesting work to go around. And, ultimately, the vast majority of organizations don't have a need for tons of people to be doing the really technical aspects of ML/AI/etc. SO much of the work is scoping problems, cleaning data, worrying about pipelines, etc... and so if OP or whomever is thinking they're going to waltz into a job and make the next version of ChatGPT, that's really unlikely with anything less than a PhD. Personally, I've found a pretty good home being able to interact with leadership to define nebulous problems and solve those problems with whatever tool is appropriate-- and my success has way more to do with communication/project management/scoping skills than with technical skills (although both are necessary)... and I think those skills are better fostered through the more traditional programs.
As a hiring manager in the data science/ml world in healthcare, I generally think of degree programs in "data science", "machine learning", "artificial intelligence", "deep learning", etc as being less valuable than degree programs in the corresponding fields that aren't as buzz wordy. I tend to prefer candidates from backgrounds like computer science, math, applied math, statistics, or something domain specific (coming from healthcare) like epidemiology (or variation like computational epi) or bio- or biomedical informatics.
Those programs tend to show me that you're interested in and have done the more boring but foundational coursework that is often cut to make the sexy degree programs. That means that hopefully you won't be upset that 100% of your job isn't deep learning, and that you'll be better suited to pick the right tool for the job.
At one of my last jobs, there was a machine learning engineering team (all boys) and a data science team (all girls and gays) who had the same ML chops. The DS team ended up getting more models into production and more research published than the ML team because they had more "soft" skills to navigate the problems the org was facing. When someone in leadership would say "we're having issues booking appointments", the ML team would set off building some fancy deep learning model while the DS team would generate hypotheses with stakeholders, do some exploratory analysis, run a few prospective studies, and then use those results to inform some regression models that would end up in production. It wasn't as sexy as some deep learning model, but the leadership team wanted full interpretability of their model so deep learning was never going to be acceptable. I generally think of these kinds of skills being taught more the stats, applied math, or epi programs than in the designer ML programs. ymmv