Biology needs more staff scientists
nature.com
nature.com
I made a simple proposition to my PI: I'm skilled. I'm at least 3x as productive as a grad student (and I could make 10x in industry). You pay me 3x, and I'll work on your projects some of the time and mine some of the time. Give me (a realistic and workable level of) freedom, a living wage, and treat me right, and I'll stay here as long as I can. He agreed.
It takes an experienced PI to understand just how much more value a staff scientist gives over a early-stage postdoc or grad student, which are cheaper. You get stability, expertise, and experience for what is ultimately not that high a price tag. I myself have watched my PI undergo this evolution. I have been with him 10 years, and in the early days, he was all about summer students etc. Now he knows that you simply cannot get real work done with cheap, temporary labor.
I could get higher-paying or more prestigious jobs elsewhere. And I do believe I could cut it as a PI if I wanted. But I really, really value this arrangement.
It doesn't hurt that my interests have introduced him to new people and funding mechanisms that he would not have otherwise had access to.
I'm not sure how much detail you want. That was the short version. The slightly longer version is that there are huge datasets out there, and I want to understand aging by building tools and workflows for the many datasets which were not necessarily originally gathered to understand aging, but which still contain information about it. And also to work with and complement wet-lab aging researchers by giving them context or leads for experiments.
Now we are basically extending the same idea to new data types and sources, like methylation and sequencing-based data. When we ask "what should we do next", we look for basically what archives are biggest, and if they conceivably have something useful to say about aging, we put it on the queue.
The big-picture goal is to take this approach and apply it to all these different levels of organization (DNA, epigenetics, RNA, and ultimately hopefully protein) and put it all together in a cogent picture, using way more data than anyone has done before. We ask questions like "how does the aging process differ between tissues?". We also want to go "wide" by comparing species, as the whole "intervention X works in mice but not humans" is an important thing to address.
Then on the smaller scale, the system is also of use and interest to individual investigators who want to know "what's happening with my gene X with age in tissue Y?". They like to include figures from us for example if they have some findings from mice and want to give evidence that the pattern may also hold in humans.
Finally, we built the platform to be a pretty generic meta-analysis tool, so we have occasionally used it to answer questions completely unrelated to aging. Once you do all the background legwork, asking many different questions is largely a matter of changing the meta-analytic model.
Are you working on just Human data right now? I have thought about trying to propose something very close to what you're talking about, but in the environment I'm in right now there's a pretty strong bias against non-hypothesis-driven work, and unfortunately the really big picture multi-omic approach is considered to be too vaguely defined. I wish I could just say "let's collaborate!" and then make it happen so I don't end up trying to duplicate what you're doing but if only the funding model actually supported that sort of cooperation (assuming you're in the US). If you don't mind, what institution/lab are you at now? I'm at University of Rochester, trying to make a co-mentorship arrangement work between a lab that works with C elegans as a model for aging (where the PI has no informatics experience) and a lab that works on computational modeling of biological networks (where the PI has no experience in aging/C elegans) because we don't have a real "bioinformatics" program.
Also if you have any advice on bridging the gap between biologists and computationalists, that's something I have yet to master in my now more than half-decade of experience of trying...
The way we get around the bias for (supposedly) hypothesis-driven work is to get a lot of our funding on collaborative and center grants. We can augment our local aging research work by providing them with context and pretty figures for their papers and grants, and they are happy to send a few $100K our way in exchange. We got a Shock Center award a year or two ago which has accelerated things a lot. A lot of it has been catalyzed by Arlan Richardson and Holly van Remmen, who came here from UT San Antonio.
When it comes to "bridging the gap", yes, it is hard. The key I guess is to be flexible and understand what they want. A lot of them are not looking for grand, informatics-driven hypotheses, but they are ecstatic about extra figures or data they can put into their papers to spice them up. Sometimes I slum it a little and do routine statistical analyses on their experiments just to keep the connections live.
In general, I think it is sensible to try to design systems in such a way they bridge multiple species from C elegans to rodents to humans. If you do one, it's not that hard to do them all, and they all have their place, as C elegans is good for quick testing of lifespan stuff but may or may not generalize, so it is considered helpful if you can use data to give tips about what may or may not translate.
The best brief advice I can give is to figure out what the wet lab people want, and give it to them in a way that maximizes the overlap between what you are doing and what they are doing.
If enough people in our generation take this approach, I think it will be very good for science. It is a happy medium between the old system of "everyone gets tenure eventually" and "no-job-security eternal postdoc" of the last few decades.
Science is inherently risky enough without also forcing everyone to essentially become an entrepreneur. That phenomenon is likely behind so much of the exaggerated findings that are getting published.
Most PIs I know would prefer to spend more time in their lab.
"Hire me to do the things you wish you were doing, and in the process damn yourself not to be able to do them" is a steep ask for a lot of labs, especially small ones.
So, in other words, someone perfectly suited to be a PI, a more prestigious and better-paying position with more freedom.
Our institution has one you can ask to help write/review/correct grants. They are pretty useless because they cannot possibly understand the science each individual lab is engaged in at an adequate level. Usually they just offer superficial suggestions/corrections.
Ultimately, though, I'm afraid I can't summon too much sympathy for a PI who thinks this way, because they knew exactly what they were getting into when they accepted that promotion. It is common knowledge that the tradeoff you make upon becoming PI is that you get the authority to direct the research aims while sacrificing the ability to do them yourself.
Exaggerating somewhat, a PI gets 100% of the intellectual freedom and responsibility, and has 0% of the fun. A grad student or postdoc gets 0% and 100%. I was looking for something like 50%/50%.
Right now, rather than being at 100/0, I'm actually at something like 100/50.
Hiring a staff scientist will send me towards 100/0. Which means not only do I have to think "Can I afford you?" but "Do I want to afford you?"
The amount of time you have to spend writing grants is a function of: 1) how much money you are burning, 2) how much help do you have writing, and 3) how good is your science (better science will be more successful).
No doubt I hurt on #1. But I think I help enough on #2 and #3 to compensate, and therefore I don't think my PI spends a net larger percentage of time writing with me than he would without me. Anecdotally, over my time with him, I think has been a slow decrease in the fraction of his time he spends writing.
I think there's a space where yes, your impact on #1 is probably offset by gains in #2 and #3. But there's also a space, where I'm at right now (and I suspect many new investigators are) where your impact on #1 either moves a staff scientist outside the realm of possibility, or simply dramatically increases the likelihood of stochastic funding failure unless you're literally god's gift to the research world.
Which really demonstrates how important it is to reform the way science is funded.
In the meantime, I'm shaking the couch cushions to be able to support more than one student.
You want 3X what a grad student makes makes? That makes you almost as expensive as me, and I'm the PI. I don't say that out of arrogance - I say that because that means I now have to pay for both of us while you go do science. In a grant, you're actually more expensive, because some of my salary is covered by the state/teaching/etc. while you're a pure soft money hire.
That's also ~45% of a non-modular NIH R01's yearly budget once you include benefits. Which means I'd be frantically writing grants with ~10% pay lines (being optimistic - the effective pay line for a PI under 40 is laughable) just to keep your paycheck coming. Because I can't fund you via a training grant, or an RA/TA line.
Keeping you employed for three years would consume my entire startup package. No travel, no data acquisition, no lab equipment...just you.
I'd like to be able to do all that. I'd also like a unicorn.
We also have had much more luck with center and collaborative grants than R01-style individual grants. On collaborative grants, our sections are almost always the highest scored, on top of the fact that the pay lines are inherently higher. On individual grants, it is hit-or-miss to say the least, because reviewers seem to think, somewhat rightly, "why do you need $1M to play on computers?".
People come to us and say, "hey, I want to put you in as a small section for $75K, $100K, or whatever". Over the course of time it has meant we have a diverse and relatively stable revenue stream and low overhead, and there is less risk (because less time spent) on each individual grant. Although amusingly, my PI is still associate despite pulling in way more money overall than many fulls, because of this idea that only PI grants count for promotion.
> I say that because that means I now have to pay for both of us while you go do science.
Yeah, although I said in my OP that "I just want to do science", in reality I write parts of grants, mostly the technical parts, all the time, and I go to grant strategy meetings, where I feel my input is valued as I can often give a better idea of what's feasible than the PIs can. I guess more precisely, I don't want the responsibility and pressure of being in charge of the whole package, and I don't like dealing with all the paperwork. So in our case the funding is a team effort as well.
> way things like training grants are structured
I have been with him since grad school. In the early days, the way we got around this was titling me as a postdoc and paying me like a staff scientist, which was fine by me.
Ironically, I'm in just such a field as well. But that also causes another bit of pressure, because it does mean that I could stop sooner and go back to science if I wanted to fund less people. Covering myself and a grad student can be done with a few bits and pieces of funding. As I noted in the back of napkin calculation, a staff scientist vastly increases the amount of money I need to find.
> Yeah, although I said in my OP that "I just want to do science", in reality I write parts of grants, mostly the technical parts, all the time, and I go to grant strategy meetings, where I feel my input is valued as I can often give a better idea of what's feasible than the PIs can. I guess more precisely, I don't want the responsibility and pressure of being in charge of the whole package, and I don't like dealing with all the paperwork. So in our case the funding is a team effort as well.
In fairness, I reacted to this because I have met some people who meant it seriously - they want to be left alone in a lab to answer their questions while someone else keeps the lights on. And well...yeah...you just described the perfect job.
> I have been with him since grad school. In the early days, the way we got around this was titling me as a postdoc and paying me like a staff scientist, which was fine by me.
And this hasn't backfired by having one of his "postdocs" not progressing very fast?
Well, that IS what I want, probably what everyone wants. I don't like writing grants, as I said, but I am in touch with reality and consider it an important part of my value proposition. Plus, I like my PI and consider him my friend. It's not nice to leave all the shit work for your friend.
EDIT: In many ways, I have tried to alter my behavior to prevent exactly the kinds of problem you are talking about. I absolutely want my PI to feel like my presence is a net benefit. If a staff scientist didn't understand these realities, they could easily be both naive and unhelpful.
> And this hasn't backfired by having one of his "postdocs" not progressing very fast?
Interesting point, one I hadn't considered. We are a pretty small lab, 4 grad students, 1 postdoc, occasional summer/rotation/temp postdocs or students, and me. In the past we had 2 postdocs.
The 1 postdoc who is in the lab now is a statistician who has zero interest in his career or ever being a PI. He is extremely valuable but I don't get the impression he feels held back. He is a "give me a job and I'll do it" type.
For informatics, I believe the productivity multiplier for skill is higher than in wet lab. Thus, it is not only easier but potentially better to have a small lab with high skill than the converse. So we have little interest in rapid growth.
After some trial and error bouncing around in industry/postdoc/etc., I've found that being a PI in a field where I have to raise relatively small amounts of money (compared to Big Science labs) to be the best balance for me at this point. I agree with Fomite that unless I magically get amazing at grant-writing, there's no way that I could afford to sustain full-time staff ... I don't envision myself going down this path since I want to minimize time spent writing grants and maximize my research/teaching/service time.
Clearly it is ridiculous to expect I can do what I want 100% of the time without any of the responsibility that normally goes with being a PI. Instead, I accept a middle ground of autonomy and responsibility between postdoc/student and PI.
Your article largely seems to be about why employers won't sponsor pure unicorn jobs. Quite true. That's why I have to continually make it clear what value I provide to the bottom line. But I have still done better than the 10-30% you talk about. It's probably 50-70%.
I have found that science and life in general can be quite flexible if you know what you want and take concrete, realistic steps to make it happen.
Good on him. But I believe a fair number of institutions now have up-or-out type rules which explicitly forbid this kind of arrangement. Certainly wouldn't have flown where I did my postdoc.
Personally, I think a well-run lab needs a combination of staff scientists, very good engineers and salespeople, with one or more visionaries at the top who define the direction of work. OpenAI is a great example of how this can work (IMHO),as they have both excellent scientists but also brilliant engineers and people that are very good at selling their vision to the world.
Science needs to be better funded with fewer strings attached to dollars. How much of the abilities of our best scientific minds goes to writing grant proposals?
Some are better at selling than others though.
It's a competitive market as it should be. To weed out the ideas that shouldn't be funded.
A better way to very ideas?
Also remember lots of "useless"/theoretical science turned out to have enormous real world uses.
You can do basic science but have very bad ideas based on very poor data which you interpreted poorly. You shouldn't be funded. There are enough people with good ideas for basic science who should rather get the money.
To get funding you have to provide a hypothesis and aims to prove/disprove with a starting justification for said hypothesis.
If your hypothesis is good compared to someone who has a poor hypothesis you should get the funding. We can't just open the funding floodgates to everyone. Not all people with graduate degrees are good at science. And only the best should be rewarded with funding.
Again, there is unfortunately an advantage if you're a good writer, but I don't think there's a better system at this point in time that we can rely on except maybe past published work.
What actually happens is you provide a research question and the way you intend to approach it.
What determines whether you get funding is a complex function of your age (don't be young), gender (don't be a woman), race (don't be non-white), history (come from a big lab with a prominent PI), whether you've been funded before, weird reviewer preference (like, seriously, whether you put citations in your specific aims), what section you went to and whether or not the review panel thinks this is worth doing, whether you lie in the comfortable space where your work is neither very safe nor particularly risky, etc.
Plus stochasticity.
We might not be able to "open the funding floodgates" to everyone, but at the moment it's hardly a meritocracy, and it's strangling junior scientists. It's been fascinating to see things evolve even during the relatively short period of my career.
Which is what I described. Except that I'm comparing two identical candidates except that the one has better science than the other, which is the genesis of my thesis: Bad science should not get funded.
All of the things you mention come into play at one degree or another depending on field. I think with some experience can agree to this, but even with this, I don't see how an alternative approach would make it better than what we currently have.
Evolution is really what's happening, and from my perspective for the better.
> Age
Advancing one funeral at a time...
> Gender.
The effect of this is field specific. Biology for instance skews female and I've met some fiercely competitive female biologists who are very successful, but besides my anecdote, females are preferred in biology overall.
> Race.
My sample size is a little small but does not skew Caucasian.
> History.
Which I don't see as a bad thing. Except if a PI makes the mistake of hiring an incompetent (but very aggressive) postdoc that can't tell a nude mouse from a barbering one... I've seen this happen and yes, the person carries clout from the lab with them.
> Funded before.
I don't see this as a bad thing either. As there as numerous (though not nearly as large) sources of funding only for young scientists to help alleviate this issue.
But this + history = track record. So bad for some, but good if controlled for bad science. But this too can be overcome through aggression and politicking.
> Reviewer preference.
From my experience a lot of established scientists outsource this to younger scientists so that they can get experience judging good science.
But yes, this is all over the place. But for all the shortcomings like the randomness you mention, this is a good tool especially if the reviewer pool is somewhat diverse, but also experts, a hard combination for sure.
> Worth doing
Or competes with things they're working on in secret...
> it's hardly a meritocracy
You meant to say Utopia right?
If you don't expect it to be a meritocracy then you'll be happier in the long run. As long as the system is evolving in a good direction, which from my perspective it is, we can ignore that it is not a perfect meritocracy. But creating perfect markets is pretty hard to do in general.
This is not a good thing.
> The effect of this is field specific. Biology for instance skews female and I've met some fiercely competitive female biologists who are very successful, but besides my anecdote, females are preferred in biology overall.
Biology skews female. Success in biology grant applications does not. https://www.statnews.com/2016/07/29/women-in-science/
> My sample size is a little small but does not skew Caucasian.
NIH funding does. https://www.nih.gov/news-events/news-releases/nih-commission...
> Which I don't see as a bad thing. Except if a PI makes the mistake of hiring an incompetent (but very aggressive) postdoc that can't tell a nude mouse from a barbering one... I've seen this happen and yes, the person carries clout from the lab with them.
But this entirely undermines your concept of this being a meritocracy. The name behind your former job doesn't mean your idea is any good, but we'll give you the benefit of the doubt.
> I don't see this as a bad thing either. As there as numerous (though not nearly as large) sources of funding only for young scientists to help alleviate this issue.
They are manifestly failing.
> But this + history = track record. So bad for some, but good if controlled for bad science. But this too can be overcome through aggression and politicking.
This isn't necessarily good for science. http://science.sciencemag.org/content/354/6312/aaf5239
> From my experience a lot of established scientists outsource this to younger scientists so that they can get experience judging good science.
Not on study panels they don't.
> If you don't expect it to be a meritocracy then you'll be happier in the long run. As long as the system is evolving in a good direction, which from my perspective it is, we can ignore that it is not a perfect meritocracy. But creating perfect markets is pretty hard to do in general.
It's not evolving in a good direction. Science funding is accumulating in a smaller number of "long tail" well funded labs. Science is hemorrhaging young investigators, and especially women and PoCs. The average age of a first major independent award is climbing. Paylines are going down. https://nexus.od.nih.gov/all/2017/03/06/mid-career-investiga...
I really can't see how you can possibly look at this and go "Oh yeah, this is going good places."
That's my point, but it's not as big of a problem as blog posts make it out to be. Not small by any means though.
> Biology skews female. Success in biology grant applications does not.
I'd like to see proof of this. Let me quote some dubious lines.
> "women are underrepresented in high-ranking positions."
This is to be studied, but the statement in itself means little. Without analysis we could compare with fields where women are over represented. Analysis required.
> "The exact reasons for this attrition are debated"
Attrition? As far as I'm concerned the rates of females in higher positions, while low, has not gone down. If we mean women in science in general then that also has to be analyzed.
> “People are more likely to study issues relevant to their own health,” she said.
Nonsense. People study where there's funding. Breast cancer is studied more than prostate cancer? Give me a break.
Is there no bias? No. What affect does it currently have in funding. Your article points out, we can't tell.
> "The discordance between written feedback and scores suggests subconscious gender bias bleeds into the review process — influencing both male and female reviewers."
So because we tend to praise women more than men and accept failure in women more than men that means what exactly? We should also fund them more easily than men?
> “These findings confirm that it influences us.”
Yes, but this doesn't, by itself, mean anything negative. Which the paper admits.
> "Subconscious gender bias may lead reviewers to hold women to higher standards than men"
It doesn't seem to be possible to get this from the data. I can claim that society is much more tolerant of failure in women but I can't claim this purely from the data in this research, the converse is also true.
> "The authors' analyses suggest that subtle gender bias may continue"
The authors of the study can't even make the claim that you want them to make.
Back to your claim:
> "Success in biology grant applications does not."
The study claimed nothing about success.
> "NIH funding does." ... skew Caucasian.
Not if you're American. Read the link you left here.
I would personally highly agree with this line from your link: "While further examination is needed to identify causes for the differences in NIH R01 grant success, one possibility the authors cite is that the quality of educational and mentoring experience"
But that gets political.
If we're talking about immigrants then again African and West Indies education is sub par (I'm African.) which is a major confounding factor.
> The name behind your former job doesn't mean your idea is any good, but we'll give you the benefit of the doubt.
No, but it means that you likely got better training than someone from an underfunded non-competitive environment. This is one way to judge (even though it is not a perfect test) someone's likelihood of being a good scientist. I think you would agree that good training plays a monumental role in developing a good scientist.
> They are manifestly failing. (Funding for young scientists.)
How so?
> Not on study panels they don't.
Not really relevant to what I was saying about reviews.
> This isn't necessarily good for science. http://science.sciencemag.org/content/354/6312/aaf5239 (in response to track record being important).
I didn't claim it was perfect. Nor did I claim that it will lead to discovering and funding scientists that will have great impact and letting those sometimes fall through the cracks.
What would better? If random selection is better then we can start having an argument. Not that we could really test this.
> Science is hemorrhaging young investigators
* mid career.
And this should follow with a question (that isn't answered by the research you cite): why?
> , and especially women and
Do we have any proof of this? If you were to say "attract" instead of "hemorrhage" I may more easily agree. But in some fields which females seem to prefer this is overwhelmingly false, see health.
> PoCs.
This does not seem to be the fault of the funding institutions.
> I really can't see how you can possibly look at this and go "Oh yeah, this is going good places."
Because institutions like the NIH are making evolutionary strides at allocating money to good science, slowly removing the benefit of unethical behaviour among other things.
Most of the problems you cite though are not related to the funding institutions but to externalities.
Bias, education, governmental funding etc.
Also, some scientists do overhype their ideas, and a very few might make up data, but the majority do neither.
And just because you fail at your attempt does not make your initial point void.
I did mention that there are people with salesmanship and that's an advantage that you really can't control for. Well except through scrutiny of only the science, which seems to be fairly well done through a group of scientists who have to approve proposals.
"Oversell ideas and falsify data": Of course a competitive market will encourage these behaviors. But just because there are unethical people in science doesn't mean that we should do away with the system.
I would add to "falsify data", misinterpret and misunderstand data (and specifically the statistics involved). Again, something you can control for to some degree with scrutiny of the applications by other good scientists.
Any system you implement creates incentives, these need to be properly aligned to ensure an effective and humane system. If your system encourages or rewards unethical behaviour that is a big problem.
Having applied for and reviewed grants, the idea of 'good scientists' scrutinising a proposal carefully and controlling for all these factors is a bit fantastical. Maybe in your area it's different.
This looks an awful lot like survivor bias.
Tons of money was also put into well established research to move it along. And ideas that were stupid also didn't get any funding and struggled.
> If your system encourages or rewards unethical behaviour that is a big problem.
That's markets and that's why we have a justice system. A reason why the NIH now requires grant applications to be of publishable quality to help diminish completely turnaround in experimental design after grant have been issued. A part of the evolution that I speak of.
> the idea of 'good scientists' scrutinising a proposal carefully and controlling for all these factors is a bit fantastical.
I doubt you want to do away with peer review. I'll be the first to say that It's not perfect, but it's gotten us pretty far.
"Good scientists" have their orthodoxy and "know" certain things to be to true and won't (most of the time) fund anything that goes against that orthodoxy. I.e. one funeral at a time.
Well funded PIs have the ability to fund this type of science and many do.
I think we're pretty much in agreement though.
The system is slow, not great, but like another commenter suggested, the best we can do.
I'm not sure it's survivorship bias.
H. pylori, for example, actually is a major cause of gastric ulcers. If we were actually good at identifying good ideas, Marshall and Warren would have had a pretty steady rise to fame over the course of their careers since they had, in fact, figured it out. For what I've heard, they did not--they were treated pretty shabbily for a while, then the field did an about-face and they won a Nobel prize. It's not the case that anyone who (somehow) survived that long gets a Nobel!
> I doubt you want to do away with peer review. I don't think anyone wants to completely do away with the idea of having others sanity-check their research. However, there is a huge gap between peer review as an abstract concept and peer review as currently practiced in NIH study sections. Personally, I think that there's a lot of room for improvement in the latter. For example, you could imagine giving program managers more room to actually manage a portfolio of research and (e.g.) hedge across multiple hypotheses.
I'm referring to good ideas being underfunded for a time. Only good ideas survive. Bad ideas obviously don't get to a point where they turn into something accepted and widely applicable (I'm sure there's a countercase or two...)
So only the good survive. Does that mean we need to fund across the board? That's just not feasible, so we have some good ideas that go unfunded, but a whole lot of bad ideas that do not.
> manage a portfolio of research and (e.g.) hedge across multiple hypotheses.
I see this happening in well funded labs. Taking a bit of money from here and some from there in things that are novel, but ethically related enough to be able to be justified.
But the ethics involved makes this dangerous.
You bring up good points.
Then why do the grant agencies have a constantly-declining acceptance rate, and budgets that basically stay constant every year in real dollars while the economy as a whole grows?
Something being well funded does not mean that that funding will improve over time.
If R&D is 2% of GDP and declines slightly over time but remains in the top 10 we can still call it very well funded.
And just because acceptance rate is declining and competition is increasing still does not mean that science is not very well funded in the U.S.
As a post-doctoral fellow who would rather not be a principle investigator, I would love a job where I can work independently on my own research projects with a reasonable wage.
That's about as liberal an R&D policy as I've seen anywhere.
The thing is, as a PI, someone has to do this. And while I too would like to spend my time working independently on research projects and not having to do time consuming lab support stuff I'd rather not do, I also need to be writing grants to keep my staff scientists paid.
Also, the whole argument to staff scientists representing institutional knowledge also applies to support stuff. "We don't order from X any more for reason Y." "Collaborators at Other University are usually two months late, built that into the timeline", etc.
FTFY, since I think you just described the problem with the low-wage economy as a whole. But it's especially bad in science, where innovation and knowledge are the core competency.
Actually what would be nice to try is a system like the way the polio vaccine was created. At the beginning there were a whole lot of technical and scientific questions that needed to be answered and the march of dimes organisation handed out grants to the various scientists to answer all these questions. One of the biggest problems with the current system is the knowledge gathered often has large gaps that make it impossible to use - no one wants to work on all the boring, gap-filling work that is critical to using a breakthrough.
Hell, if I could hire grad-school quality new cs grads for 20k, I'd put up with the necessary mentoring and supervision they'd need.
A grad student is never a replacement for a staff scientist. Grad students aren't particularly useful until 4-5 years into a 7 year program, and then they're out in a couple years, so they can't do complex, long-running research.
Not that we don't generally need more staff scientists, but the national labs are probably the best gig along those lines available, and due to their mission, they are very focused on CS/Engineering /Physics
In my limited experience, the life sciences are much more depressed in terms of wages.
I suppose what I'm saying supports you, in that I had to specifically be titled CS to get the higher salary. I would agree that in general, life sciences wages are depressed, but then, there are far more people in life sciences than CS, but much more demand for CS, and CS people bring in more dollars/hour (in terms of company revenue in the case of a biotech vs. a SV computer company). You can't fix that without making structural changes.
There's no RA/TA lines available for them. There's no "training grant" for staff scientists, etc.
Unless there is a paradigm shift I can't see this changing either. Look at Medicine, as fas as I know the AMA controls the number of doctors that get educated, they aren't relying on cheap student/postdoc labor. In the end more of them gets jobs as doctors (unlike the large fraction of PhD scientists who leave academia/science entirely) and usually command substantially higher salaries that what PhDs make with similar amount of time spent in school.
I once worked in a large R&D facility for hydraulic equipment. The place was about 75% blue collar workers - good ones, because this was all one-off stuff, not production. Only about 20% of the work force had engineering degrees.
Actually, you probably wouldn't even trust a PhD student or junior postdoc to setup lab equipment...
I can make close to 7-figures (amortized over a decade or so) pushing/enabling the AI/Cloud Computing agenda...
or...
I can make close to 6-figures (and stably so) working as a staff scientist...
Gonna have to split the difference there IMO to grow the ranks. I am somehow reminded of the article complaining that retirees are hoarding cash instead of spending because they fear the future.
How about constructing a society where I feel I should work for those 6-figures because it will bring a better future rather than compelled to chase 7-figures because the future is @#$%ing scary right now? Because that's what it's going to take for someone like me. And of course, I only speak for myself, but the data in that article about retirees makes me think I'm not the only one.
Join a lot of startups that ultimately fail, but which were doing the right sort of things... Some of them didn't fail... Repeat with ever-increasing ability to detect bozos...
Mostly you make very low 6 figures.
Broadly, there are two mistakes I see people make:
1. Not taking enough chances in the first place
2. Taking a chance, but riding a loser all the way down or swearing off startups altogether after a single bad experience which leads to 1 above
You are a most likely an at-will employee. Your employer can dump you without any warning at any time. Any sense of loyalty beyond enjoying your work or your paycheck is misplaced.
Also, you could argue legitimately that I just eventually got lucky and I represent a raging example of confirmation bias now, but it's happened 3 times now, so maybe I've learned a thing or two by getting burned a lot? Or maybe not. Truth is a pathless land and all that.
One sentence advice: go work for Google, Amazon, Facebook, Apple or their ilk if you want a relatively safe high six-figure income and you can't stomach risk.
Several of my friends are in PhD programs, work far harder than me, and are most likely going to be floating around in post-docs for years after. I wish we had a congress that better prioritized science funding - basic research is the foundation for a lot of the advances we make as a society, and it's so stressful to be in academia right now that a lot of extremely talented and skilled people burn out and take industry jobs.
It is further distorted by lacing what would be a better collaborative allocation of resources with instead strong flavors of formalized individual protectionism, whether student or PI. So now your limited funding gets spread to 10 groups secretly doing the same thing and they have to take a lot of shortcuts to "win."
> All institutions should be like Broad (paraphrasing)
> The Broad Institute attracts world-class scientists, as both faculty members and staff.
HHMI's Janelia, the Salk Institute, and others that function like Broad can all attract world-class scientists. I'm skeptical that they're producing better/more interesting science because of their staff-scientist heavy model. A simpler explanation is that they attract better scientists and have almost unlimited funding.
In the end we arranged things so I would work 1-2 day a week for them, while keeping my day job. I ended up helping a few other postdocs with their code. I do have a PhD in physics, but in that position I was free to ignore the deeper part of the science (like reading literature, figuring out hypotheses) and I would mostly concerned in helping them set up their numerical experiments, or symbolic computations.
The downside is that the pay is rather poor, at least for me in the life sciences. Also don't expect to be treated with the same respect as tenured faculty.
It's refreshing, in an article about needing more technical workers, to see at least some glimmer of a nod to the realities of supply and demand. I can't say this article quite recognizes this reality, but in its defense, we are talking about science, were people openly state they are motivated by the opportunity to do good work rather than salary alone.
So, this is different from a startup founder looking to get rich complaining about a lack of engineers to hire, without mentioning pay. But not completely different, only a little different.
"Biology needs more staff scientists." You sure you don't mean more programmers at low pay? Much of the work requires increasingly sophisticated programming ability, a skillset that is not really taught in or rewarded in an academic career path. It sounds like biology needs more people who will work for half of what they could make in industry, in a field structured to ensure they will never rise to leadership positions.
I might as well say that biology needs more disposable young programmers who are willing to work for $5 an hour. I might as well say there is high unemployment among biology staff scientists who won't work for less than $250k a year.
At a low salary, the market will demand a lot of a service, but there is little incentive to supply it. At a very high salary, the market will demand much less, but there will be. much higher incentive to supply it.
There's also an elephant in the room here - university positions are often exempt from H1B caps, which means they can bring in programmers who aren't free to participate in free labor markets. As long as that is the case, don't expect supply and demand curves to meet. Markets only work if people are broadly free (not just to take another programmer position at a similar institution, but to leave the field, start a business, become an artist, install dry wall, sell real estate... you know, free, as in, well... free. I'm not sure there's any other way to put this).
This is a strange thing, but here it goes - if you ever read an article about a "shortage" of workers in a field, be very careful about going into that field. It may mean that employers are looking for ways to gain coercive control over their workers lives, and to create a captive workforce that is not free to go into other fields. And that sort of control often spreads into abusive work relationships, poor working conditions, and other very ugly situations.