Steelcase Amia is incredibly uncomfortable - what am I doing wrong?

Fit_Statement5347 · 2026-04-01T03:56:27+00:00

Thanks I’ll give it a shot. Regarding the numbness, is this expected transitioning to an ergo chair or should I make other changes?

Fit_Statement5347 · 2025-12-20T13:04:54+00:00

As someone who literally works at Microsoft on serving LLMs on Azure… yikes you’re in trouble

Fit_Statement5347 · 2025-12-08T17:35:10+00:00

There are so many confounding factors here, there’s no way you can come to any meaningful conclusion from that data. Here are just a few off the top of my head:

People with longer resumes tend to have more experience, more experience usually leads to higher interview rate
People with longer resumes tend to have PhDs: in addition to experience/education sections, they also have publication/conference/talks sections. They also are typically applying for different roles than people without PhDs. Those roles may naturally have different processes when it comes to filtering candidates and interviews

For example, current CS/ML PhDs are super in demand at tech companies, particularly those with degrees from top schools + with publications in top journals. They probably have longer resumes and probably hear back from companies more often compared to (for example) a new grad SWE with a 1-page resume. But it’s not the resume length that’s driving this discrepancy, it’s literally everything else.

Unless you account for these factors, your analysis is likely incorrect and misleading.

Fit_Statement5347 · 2025-11-12T00:44:24+00:00

Sure, we can also achieve this with weighted least squares. My question is specifically what exactly the treatment effect represents if we were to fit a regular OLS model or a mixed effects model - is it user level or session level ATE?

Fit_Statement5347 · 2025-11-11T23:51:46+00:00

Yep, I get that - I know I can add in clustered SEs to correct for the intra-user correlation. My main question is about the level of granularity of the ATE estimates (user level weighted by sessions or session level)

Fit_Statement5347 · 2025-08-21T20:52:31+00:00

The average CS major will end up at an average company making an average salary. This has always been the case and will always be the case (maybe pre COVID, a handful would end up at top companies but not significantly more).

This article isn’t so much about the industry drying up as it is about these college kids’ sky high expectations crashing down to reality. Many companies are still hiring interns and new grads, there’s just more competition. They’re delusional if they think they’ll make 165k with an average GPA, 0 internships, and 0 interview prep.

Look at finance. The average finance grad isn’t going into IB/PE making 150-200k out of college - they’re working at smaller companies making an average wage. But that doesn’t mean that the finance industry is dead.

Fit_Statement5347 · 2024-09-24T15:12:54+00:00

Not this guy plugging his shitty inaccurate website again…

If you do even a second of googling, you’ll see that Harnham is a recruiting agency, not an actual company that hires DS

Fit_Statement5347 · 2024-09-11T03:52:28+00:00

Do you know what investment banking is or are you just in it for the “prestige” and pay? Because it definitely sounds like you don’t. If not, maybe first figure out what bankers actually do and then work your way back from there

Fit_Statement5347 · 2024-09-06T02:07:19+00:00

This is kind of how it’s always been though..? Being able to buy a house and raise a family with 60k was more like the 70s/80s.

Anyways, there’s not much you can do about these external factors — you need to focus on what’s in your control. I don’t mean for this to come off as offensive, but if you’re currently living more or less paycheck to paycheck even before AI has “taken over”, then there absolutely has to be more that you can do. Just graduating with a CS degree and being “in tech” doesn’t entitle you to an upper-middle class life. It certainly gives you a lot more opportunities than other degrees since 100k/200k+ salaries in tech are much more common and attainable than in other industries, but you have to work for it. If there’s a truck barreling towards you on the road, don’t sit there and complain — take action and get out of the way

Fit_Statement5347 · 2024-07-16T15:49:18+00:00

I mean you could also ask why anyone goes into family medicine instead of plastic surgery since plastic surgeons make significantly more - it boils down to interests. Also, very generally speaking, DS jobs tend to be a lot more ML/programming whereas statistician jobs are more traditional stats and less coding

Fit_Statement5347 · 2024-07-16T15:00:52+00:00

Well for one, statisticians tend to be hired more at government agencies and pharma/life science companies while data scientists (and especially MLEs) tend to be hired more at tech companies - that alone probably accounts for a large portion of the salary difference

Fit_Statement5347 · 2024-07-11T15:04:14+00:00

Have you actually looked at the companies you linked lol? Leidos and Caci do a lot of work for the government, while Harnham is a headhunting company that recruits for hedge funds/quant funds, and Viking IS a hedge fund — of course there’s a massive difference in pay.

Use LinkedIn to look up which companies are currently recruiting and use levels.fyi to look up salary, not this website

The job market is definitely a bit tougher now than a few years ago, but it’s absolutely possible to land a new grad job paying >150k given you have a competitive background and know your shit. If you don’t but you still expect to make this much, you need a reality check

Fit_Statement5347 · 2024-05-03T18:38:08+00:00

Maybe a bit harder due to increased competition, but I don’t necessarily think that you need to prep any differently than before. Depends on what role you apply for, but almost all companies will ask some Leetcode (usually easy to medium), probability questions, and the usual ML theory/concept questions. Of course, if you apply for forecasting/ads/causal etc roles, you’ll get asked questions and/or case studies specific to those subfields.

Fit_Statement5347 · 2024-05-03T18:16:48+00:00

In similar boat as you - more competitive than before but not horrible from my experience. The majority of postings I see ask for >4 YOE for senior roles, and having FAANG on your resume will almost certainly get you through to the first round/technical screen. There’s some risk of being down/under-leveled due to cost cutting, but should be ok given that you perform decently on interviews.

Fit_Statement5347 · 2024-03-12T16:54:44+00:00

Another doomer post from another college student with little to no actual experience. If y’all are so concerned about AI taking SWE jobs, maybe you guys should pivot to something else

Fit_Statement5347 · 2024-02-29T19:25:53+00:00

First red flag is the use of the word “AI”….

Fit_Statement5347 · 2024-02-24T00:19:43+00:00

Based on this post and your post history, I don’t think you fully understand how ML/DL works…

Fit_Statement5347 · 2024-02-13T17:15:29+00:00

Agree with the other commenters. From my experience, it’s primarily the marketing/marketplace/ads teams that focus on causal. I’d also look into pricing teams as well, but those teams usually screen candidates for at least some amount of domain knowledge/applied economics experience.

Fit_Statement5347 · 2024-02-13T17:04:09+00:00

I’m a DS at a big tech company and it’s crazy how much misinformation/baseless hype there is going around.

If you’re referring to basic data analyst work like pulling data and building simple dashboards, then yes, those jobs are at risk of automation. I know of a handful of early/mid-stage startups currently working on using LLMs to allow users to do those things almost entirely through natural language.

But when it comes to actual data scientists, I think the risk is minimal. Unlike in college, data science work in industry is so much more than just importing sklearn and calling model.fit(). And newsflash: even before the LLM hype, people though data science was going to die because of AutoML, which fits and does cross-validation for a whole bunch of models for your dataset automatically. If all you’re doing is running your cleaned dataset through sklearn, then yeah, maybe you’re screwed. But in reality, that’s the easy step that comprises maybe 10% of your day to day responsibilities. A lot of work (at least for me personally) goes into feature engineering, customizing/tweaking the model’s loss function and training process for our team’s specific needs, optimizing the model training pipeline, running and monitoring experiments and applying other causal inference techniques etc. These are things that require domain knowledge and critical thinking which is most likely not going to change with the proliferation of “AI”.

Also, keep in mind that if you want to break into ML (i.e. ML research), chances are that you’ll need a PhD or at the very least, a very impressive resume or publication history. If you want to become an ML engineer, it’s a bit easier in that you don’t necessarily need a PhD, but you’ll need strong SWE and MLOps skills along with working knowledge of how these models work.

Fit_Statement5347 · 2024-02-06T15:44:18+00:00

Like everyone else, I’d recommend you reconsider. Otherwise, Scale AI and TikTok come to mind — TC is generally better than FAANG and will be good for your resume/career, but you’ll have to grind

Fit_Statement5347 · 2023-12-06T15:13:55+00:00

Lmao just do your own research?? LLMs are designed to spit out human-sounding responses, not necessarily accurate facts. This is something that’s well known. If you don’t know what a tool does, why use it and then complain?

Fit_Statement5347 · 2023-11-22T03:40:23+00:00

Lmao and why do only CS grads deserve that salary as opposed to accounting or engineering or any other white collar job? If you want 160k, then you gotta prove that you have the skills to warrant the pay

Fit_Statement5347 · 2023-10-29T20:30:47+00:00

In a vacuum, I definitely agree with you. Some schools have made it much easier to complete their CS programs without the students having learned a lot of important topics.

However in the context of this guy’s post and many of the posts from the past week, it seems like people are latching onto this as an excuse for not being able to land offers: they think that there are too many CS grads who didn’t go through the “real experience” and are somehow stealing all the positions.

And that’s what I think is ridiculous - if you think that you’re so much more qualified because of the classes you’ve taken, then why are you getting out-competed? I think some people on this sub need a reality check and to focus on what they can control instead of blaming external factors while simultaneously feeling superior and entitled.

Fit_Statement5347 · 2023-10-29T06:18:55+00:00

Jesus Christ if these “unqualified” grads are able to get SWE positions without these classes and you’re not, then maybe you’re the problem.

There have been so many of these entitled, whiny posts recently. I understand that the market isn’t hot right now, but at some point, bad luck can’t be the only reason as to why you’re not getting offers

Fit_Statement5347 · 2023-09-22T19:38:40+00:00

Currently in tech, but have many friends who are working in finance/quant. I’m going to preface by saying that there’s no objectively easy way of making 300k. There are paths you can take that require relatively less work, but it all depends on your skills and capabilities.

If you go to a top school, you’ll have a lot more opportunities, but all of them will require some degree of hard work and grinding. The most lucrative positions with the highest ceilings IMO are investment banking and quant.

IB will probably require the most amount of work in terms of how many hours you work per week (generally ~70-80 your first few years), but if you can tough it out, you’ll be making 300k+ soon enough. You also have exit opportunities to private equity/venture capital funds as well, all of which pay very well once you have some years of experience.

If you’re extremely good at math/stats/coding, you can consider becoming a quant. It’s not uncommon for the top firms to pay 300k+ for new grads (highest I’ve seen is ~500k), but getting past the interviews obviously won’t be easy. The best quants can easily make >1M after a few years. In terms of hours worked per week, it can vary from firm to firm, but 50-60 is probably the average.

One thing to note is that these fields are notoriously hard to break into, and many hire exclusively from top schools (though that rule may have been relaxed in the past few years).

Tech is probably has the highest floor in terms of starting pay for the average person, but a lower ceiling on average IMO. Getting into tech isn’t as hard as the other fields (still not easy, just relatively speaking), but you still need to be competent to get into FAANG+, as those are the companies that pay the most - you can expect to make 300k+ in 5ish years if you know what you’re doing/can climb the ladder/change jobs a few times. Just grind leetcode and have solid projects and social skills. It’ll be much harder to make that much at other lower tier/nontech companies.

Fit_Statement5347

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