This essay was written for the Astera Institute’s “Identifying Systemic Bottlenecks to Science” essay competition.
I show up to work early. I set the coffee maker going, but I don’t have time to wait for it. Instead, I make a beeline for the mouse facility.
It’s already gone 8 a.m. and I’m late to feeding my mice. We’re running a time-restricted feeding experiment whereby the experimental group only has access to food between 8 a.m. and 5 p.m.—shown to modulate liver metabolism in a manner that might affect the development of hepatocellular carcinoma (which is what we’re testing). Today it’s my turn to do the cage changes. I don my PPE and get to work: I take the cages out of their light-tight cabinets, put them on the sanitised bench-top next to their food-containing counterparts, then open up each pair of cages and transfer the mice from their fasting cage to their feeding cage. Cages back into the cabinets, wipe down the surface, then back to the main lab.
I head straight to the cell culture room, where I aliquot some medium into falcon tubes and put them in the bead bath to warm up to 37°C.
I briefly return to my desk. Just enough time to clear emails, check my calendar, and savour my coffee (I’m halfway through a bag of lightly roasted washed Ethiopian beans).
I head back to the cell culture room and spend the rest of the morning setting up an assay. Essentially, I’m wearing PPE, moving liquids around (sometimes with cells in them) using tools like the P1000 pipette, and operating specialised equipment like the 5810 R bench-top centrifuge. It’s all manual, physical stuff.
Back to my desk for lunch during an incubation period. I scoff my greek yoghurt and granola, deal with emails again, and read up to figure 2 of a paper that had piqued my interest. I’ll finish it later perhaps.
The afternoon is a bit slower: I’m staining cells with fluorescent antibodies so I can quantify specific proteins in the cells via flow cytometry. I spend the incubation periods analysing data, working on figures for a paper I’m writing, and stopping myself from doomscrolling on X (guilty!).
I make it to the cytometer with my samples by 4 p.m. I sit there for an hour or so setting up the instrument, calibrating it, tweaking the voltages to put my fluorescent cells nicely in the middle of the dynamic range of the detectors, and then acquiring and recording the data. I export the .fcs files then clean and shut down the instrument.
5 p.m. strikes and I’m off to the mouse room again. Same as before. Yoinking the fellas into their fasting cages for the evening now. I clean up. Disposable PPE goes in the bin. Reusable PPE into the autoclave basket. I bring my laptop home so I can finish data analysis after dinner.
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This is a fairly typical day-in-the-life. I spent maybe an hour and a half at a desk and clocked 14,000 steps while charging around the institute. On any given day, the tasks I do typically fit into one of the following categories:
animal handling (including surgery/
procedures)
moving precise volumes of various liquids into various containers
operating machines—sophisticated and simple alike
data analysis
writing
reading
Interestingly, only the latter two of these tasks did I have any training for during my far-too-many university degrees. The majority of what I actually do as a life scientist was learned on the job. And a lot of it is physical labour, albeit more taxing of one’s dextrousness than one’s strength or endurance.
Perhaps this is why it has been argued that scientists are, in fact, blue-collar workers merely coded as a white-collar workers. It’s a provocative yet persuasive claim. We wrangle animals like farmers do. Like factory workers, we operate sophisticated machinery and face replacement by robots1. As electricians and plumbers do, we learn vocationally. I suppose we also move liquids around akin to a car mechanic—although our liquids are typically more aqueous.
And yet, despite this, our hiring process is indistinguishable from that of a management consultancy firm. To my mind this is an obvious mismatch and leads to poor hiring decisions which, especially in academia, can be de facto irreversible. The net result of this misaligned hiring process is that we lose great scientists and retain incompetent ones who are good at Zoom interviews but bad at physical science.
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I recently sat on an interview panel for our lab. We were trying to hire a new research assistant. HR did the primary screening process, and we then were given the resumés of a shortlist of candidates. We interviewed each one via Zoom. Predictable, stale questions:
“Are you comfortable working independently?”
“How much experience do you have handling mice?”
“What steps do you take to mitigate cell culture contamination?”
It was a frustrating process because it was just impossible to get what I wanted from it. What I really wanted to know is how competent they are with a pipette in their hands. How good they are at juggling two protocols simultaneously. How confidently they can perform intra-peritoneal injections. Under our current hiring paradigm I can’t know these things until they’ve already been hired.
The frustration here highlights the need to reform the hiring process for wet-lab science, which, in its current form, is a huge bottleneck to scientific progress and an impasse for meritocracy. I propose trialling a hiring process that emphasises vocational competency. The reformed process would look something like this:
Initial screening of candidates is done by HR department (same as before)
A shortlist of candidates is invited to Zoom interview. Now, rather than being asked asinine HR-ified questions (“tell me about a time you overcame something difficult”), the candidates are given a single task to do live, while sharing their screen: design an experiment from start to finish, including creating a list of reagents to order. Candidates are asked to think out loud. Tasks here can include plasmid design, flow cytometry panel design, reagent comparison and selection (including cost-benefit consideration), etc.
The scientific question must be relevant to the lab they wish to join (an experiment that the lab actively wishes to carry out), and should be the same for all candidates for fairness. The experiment should also be doable in one or two days—not a week/months long protracted experiment. The candidates are tested on their ability to figure out how to answer the scientific question.
As an example, a T cell biologist (my field) can be asked “how would you test whether interferon-γ is secreted at the immune synapse?”. A good candidate with microscopy expertise might find validated IF/ICC antibodies, describe T cell culture, conjugation to target cells or artificial synapses, fixation parameters, staining, microscopy, and microscopy analysis details. (By the way, the answer to this question was always assumed to be “no” until very recently!)
The most promising interview candidates are then invited for in-person technical assessment. At this point, their reagent lists have been ordered to the lab and any pre-requisite animal/cell setup has already been performed by lab members. The candidates have also already completed health and safety onboarding (which removes the need for them to complete this later). They are then launched into experimental work—shadowed by a senior lab member (postdoc/staff scientist). They follow their own protocols and experimental plan. They are assessed on their ability to carry out the work faithfully and quickly. The one-day in-person visit concludes with a face-to-face discussion about the experiment and the results (if the experiment can be completed in a single day) with the PI and senior lab members.
The candidate who demonstrated the best experimental planning and execution skills is hired for a job that is primarily concerned with experimental planning and execution.
Yes, this requires a bit more investment (of time and resources) up front, but the benefits of picking the best candidate would surely pay back this investment many times over.
It would be fairly straightforward for a research institute or academic department to run a randomised trial to test whether such reforms would enhance the science produced. Labs can be the randomised into reformed vocational hiring or the status quo. Then, over a period of, say, one year, labs hire new staff as needed. The typical performance reviews that new hires undertake can then be used to test which hiring process produces better results. Outcomes can be subjective and objective metrics relating to team cohesion and scientific outcomes (e.g., surveys of lab members, contributions to papers, etc.).
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When scientists lack technical competence, they procrastinate experiments, they opt for suboptimal but technically easier experiments, and they perform experiments badly. Badly done experiments can generate falsely negative data if technical noise drowns out biological signal, or falsely positive data if technical error produces a bias in one direction. Both of these situations contribute to unreliable, irreproducible data—an enormously concerning problem for science. Even if badly done experiments are discarded and repeated until done well, money and time is wasted.
The degree to which poor physical workmanship limits the sciences is, by my reckoning, massive and under-discussed. Further, it is also a largely invisible problem to PIs, who don’t really spend time in the lab micromanaging the intricacies of the experimental work.
Given that up to 80% of NIH grant money goes towards salaries and benefits for personnel, hiring mistakes are very costly. Further, the human consequences of poor hiring processes are that someone not cut out for the job suffers the pain of being unskilled at something they shouldn’t have been hired for, while talented, skilled scientists were not hired and potentially lost to science forever if they pivot to a white-collar job instead.
In my view, reforming scientific hiring practices is an obvious, tractable, and highly impactful bottleneck to solve, with potential to increase the speed and quality of scientific research while improving job satisfaction and reducing costs. Science is a technical job. We need to hire based on scientific and technical ability.
something I am immensely looking forward to
The suggested hiring process, especially solving work related tasks while thinking out loud, sounds a lot like how software developers are interviewed.
The concept of lab techs exists and those don’t have to be the PhD scientists. If it’s PhDs doing all the pipetting then it may be because there is an oversupply of mediocre PhDs. Everyone wants maximum status. Everyone wants to be an intellectual, at least upper middle class, educated white collar. It’s the mantra everywhere. Nobody dreams of sending their kids to one day robotically move lab material and vials from one fridge to another on the command of a boss. That’s not prestigious enough. Make it dignified by the culture and they will be more willing. And or pay better. But again, if there is an oversupply, they don’t have to. And robotics will make all this go away pretty soon either way.