Part I - Should I stay or should I go
I will start this series answering what I feel is a relevant question: Is it possible to stay in academia without being a professor?
Afterwards we see some differences and what to expect in the Industry.
But before I being to address that question, and before you starting looking for your next job, I would urge you to think about what do you like doing in your current role, and what you would like to be doing in your next role. Just think of all the tasks you did last week or two, and list those that made you happy or that you were looking forward to do. The goal of this exercise is for you to understand what you would like to do in your next role and this might end up guiding your decisions.
Academia-lite positions
Maybe your motivation to leave academia it’s not that you are tired of pursuing research for the sake of knowledge, but rather that you want to do that but with a predictable, ideally long-term, contract. Fear not, there are (at least) two main options to stay in academia and stay out of the professorial rat race:
- Core (service) facility positions
- Staff scientist
Note: roles such as data managers or research software engineers also exist but I am not very familiar with those and they tend overlap in terms of position with the two above.
Core facility
Working in a core facility can be very fulfilling:
- You get to work on many different research projects thus you are constantly learning.
- You will likely be surrounded by others with a similar skill-set and thus learn with them.
- There will be opportunity to share authorship in publications. And if project fails because it wasn’t scientifically sound, the data was not acquired properly or it got scooped, your job will not depended on it.
- You will see your work acknowledged and appreciated in (good) institutions and contribute directly to scientific progress.
- It is also a good position to educate junior researchers in experimental design, statistical good practice, and tutor in courses.
Depending on the facility, the level of scientific collaboration can be very deep - co-lead projects - or there might opportunity to author software. As an example, DeepTools was developed by the Bioinformatics Facility at the Max Planck Institute for Immunobiology and Epigenetics (Freiburg). And in sequencing facilities you will learn a lot about the technology.
On the flip side:
- It’s very unlikely you will develop your own scientific project or have a major say in the scientific direction of any project you are working on.
- You might not be able to refuse a project at all (some facilities are obliged to take all projects from an institution).
- Some of the work will be repetitive, for example of dozens of differential gene expression analysis.
- After the results (report) was delivered your part in the project is completed and you might never hear about the progress of the project until publication.
In sum, a good position if you like certain parts of academia, are not attached to projects, and have a service oriented attitude. The pay level is at academic levels - though a bit higher than a postdoc - but the contracts tend to be permanent.
Staff scientist
Staff scientist positions are probably the perfect choice for those wanting to do academic research and have some job security. In these positions, you will either work within a research group or institution, which means that there will be a central theme to projects, and they won’t be as disparate as those coming through the door of a core facility. Being an independent researcher means that, within certain constraints, you will have some freedom on which projects to take on and even come up with your own. These might span from pure data analysis to get biological insights, all the way to software or method development.
The downside is that like any unicorn, these positions are very rare, at least in Europe. Some institutions have those, and more so in the USA (to the best of my knowledge) but still rare. There is also the danger to being spread too thin if you end up being the only person in that role for an entire group / Institution, thus being able to manage projects and expectations is critical. The other main concern is the lack of a formal peer group. These positions might be “too independent” and there won’t be anyone else to bounce ideas with - the lone bioinformatician issue.
A(nother) postdoc
This one might be controversial but in my opinion doing a postdoc can be beneficial in certain circumstances, even if your mind is set on leaving academia.
Imagine you have a plan for what your next career step looks like - for example using AI/ML for early-stage disease diagnostics, specially neurodegenerative. However, your PhD is in computational biology (modelling TF binding) and whilst you have some ML experience, it was just this one side project and is not enough to get an interview for jobs asking for TensorFlow experience. Or your knowledge of Neuro-biology is sketchy. Your CV might be strong enough to get a position in the industry and learn on the job but but if not, why not do a short postdoc and use it has a paid training opportunity?
Another reason to consider a postdoc are are personal circumstances. If the city where you live doesn’t have have great private sector opportunities, and one might need to wait for a partner to finish their degree, run a contract to the end, or wait until the kids finish day-care and move only when they are at kindergarten. In any of these circumstances the priority priority will be those immediate personal concerns, so using the time to improve your CV - by training in a new skill, more papers, or developing a tool / algorithm - with a salary doesn’t sound so bad.
Two caveats with this option are:
- You’ll need to choose the postdoc group and project very carefully. As a computational person finding a postdoc is fairly easy, but picking the one which allows to learn precise skills might be trickier.
- By taking a postdoc you are missing in the opportunity of earning more. To balance this out, the length is important. Make it short.
Or maybe you are really happy in your current group doing meaningful research and your PI offers you a “permanent” contract!
There is nothing either good or bad, but thinking makes it so (Hamlet)
By which I mean, your life, your choice.
Drawbacks of academic-like positions
One consideration with academia-adjacent positions, is that the opportunity for career advancement is practically zero. There is a lot of room to learn new skills, but the only available career promotion in a core facility is to become leader and that doesn’t happen often. This means that if trying new roles, or going into management appeals to you, the options are very limited. Salary will be generally raised at fixed rate but few promotion opportunities at least in Europe. In large, semi-private American institution this might be different (Broad, Chan-Zuckerberg, etc).
As hinted above, lateral career moves are also tricky. If for instance you want to stop doing analysis and focus only data engineering or software development the opportunities might not be there. Facilities have generally low staffing and there is an expectation that everyone will do a bit of everything. This situation might different in some larger institutes - the EMBL-EBI and Broad come to mind because there is a broader spectrum of roles.
Nonetheless, there is a lot of nuance in the above and some aspects of each position are specific to each institution. Some of it can be deduced from the job description, but in case of doubt, my suggestion is to apply and ask clarifying questions during the interview. For example, what scope there is to develop own projects, if there is a budget for course / conferences (and if group members actually use it), promotion opportunities, etc.
For profit companies (Industry)
Let’s talk industry - or for profit companies. I’ll start by getting salaries out of the way. By all accounts salaries are better in a company, and job security is also better - if you are based Europe (in the USA it’s a different story).
But salary differences are not what I would like to highlight as the best part of working in a company. The best is that, generally speaking, your expertise and ability to learn is valued, that’s why you were hired after all! You are an expert on something and your opinion counts. In addition, hiring is expensive! Once you are hired, the company wants you to succeed.
Another key difference is that the industry is more results oriented. Whilst in academia there is always something to improve, or another question to answer, in the industry once a project gives a solid good enough outcome it might be enough to call it a success.
Let it go, teamwork and project ownership
Your will work in a team. That can also happen in academia but there teams are generally smaller, maybe the a lead student and a posdoc or technician. In a company you might be in a meeting with a chemist, a couple of software developers, a biologist and another data scientist to discuss the latest results and how a project will progress. An individual will contribute with their specific knowledge to advance the project, but it will also depend on the contributions of many others to get the work done.
As you can imagine there will be a clear project lead, but as the project progresses, for example through the drug discovery pipeline, someone else might take over upon successful completion of a milestone. For instance a cancer biologist might be the best person to develop and hypothesis and design experiments, but once a promising compound is found a chemist is best placed to develop it further, and so on. Or a bioinformatician found the perfect biomarkers, but now it’s up to a wet-lab team, engineers and product team to make a commercial product out of it. Another example is that you developed and prototyped a new algorithm but a software development team will take it on to productionize it. You might still be part of project meetings but your role goes from main lead / developer to consultant.
Crucially, projects are evaluated at regular points, so it should come as no surprise that projects get killed if not progressing as expected (paused, parked, or whatever euphemism you prefer), and it can happen quickly. There are generally pre-defined project milestones, or key experiments, which if they fail to show the expected results means a pause in the project. This is in stark contrast with academia where “let’s do just this one experiment and I am sure we will see a difference” is common place.
Another aspect is that instead of working in single project for years, it is likely that you will have several projects on your to-do list. Furthermore, you might be asked to completely change your priorities on a very short notice. Some of us adapt to this, but others might struggle with sudden context switching. It is possible that some jobs are advertised for a specific project and you will dedicate most of your time to it, but as mentioned above there is no garantee the project still exists a few months after your starting date.
Career progression
As opposed to academia, in the industry there is generally more opportunity to make lateral career moves. For example, someone starting out as a data scientist might discover that it’s more fun to wrangle data as ask to be moved to the data engineering team. Or prefers to work closely with the wet-lab R&D and the biology and become a member of that team. Or go into management.
A start-up might even offer more opportunities to wear many different hats before settling into a role - it is sort of expected that people will do whatever the company needs and fits their skills to some extent. By moving teams or simply interacting with others, there is an opportunity to learn about other roles and functions one never even realized existed. In a bigger company roles are likely to be more defined, but the paths to change team might also be better delineated.
Being able to work in many different things or wear many hats is definitively a plus for some, but might too tasking for others. For the latter, a more established company makes more sense vs a start-up.
Meetings, nothing more than meetings
This is the big one. Compared to academia, the number of meetings is much higher. If you think about it makes sense though. Working on several projects with many stakeholders (in a team), you will have to communicate with them the progress, get information on where the data is and which data is more suitable, make sure the work is not being duplicated by another team member, etc. In sum, with teamwork comes extra communication and this is (a key) part of your job as important as coding!
Communication is a valuable skill to have. Unless you are developer working on a single project with very defined tasks, let’s say pipeline development, it’s very likely you will be interacting with colleagues to discuss your work on a more frequent basis than you used to do in academia.
Drawbacks of the Industry
As mentioned, unlike in academia where generally a person is leading project from conception to publication, this is very different in a company. So different that even seemingly successful projects, say an extremely accurate model to classify tumor samples, can get shelved for business reasons. It can be because it’s hard to commercialize, or maybe the company changed priorities from Cancer to some other therapeutic area. Regardless, this is the type of decision you will have little agency on and will have to mentally prepared to accept.
All this to say that if you have problems letting go of a project, or prefer to work alone, maybe think twice before going to a company.
I might have also painted a somewhat rosy picture of the possibilities one has to change role in a company. The path to do it may not be straightforward. The new team or manager needs to accept your internal application, and that means your old position has to be filled (basically hire someone else) and thus costs for the company. So the move needs to align with business goals and strategy and prove valuable to the company. It’s a long winded way to saying that it might take time or not be possible. On the plus side, there are many companies out there so applying to another job is always a possibility.
The team structures are also more fluid than in academia. There you have a PI, and that’s it until the end of your PhD or postdoc. Sometimes, a PhD will change a supervisor, but this is fairly rare. In a company a change in business goals might mean your team will not exist next month. You are still working for the company though, it’s just that now you move from doing analysis of genetic screens to a team making making data analysis pipelines under a different Manager.
As indicated above, flexibility is key in the industry. Depending on the company and industry the pace can be intense, so ask questions during your job interview to get an idea of what expects you.