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title: Nordic-RSE 2025 career panel

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# Nordic-RSE 2025 career panel

These are the resulting notes (audience feedback and interactive discussion) from the "career and organizations" panel.  There is minor editing and there was the chance for people to remove what they didn't want to be public.  We hope that it is useful to others it finding your career path.

## Introduction
- This is a career panel to learn from each other
- We don't spend enough time hearing about non-traditional career paths
- This was envisioned as a 5-people-in-front format but I decided that's too limiting

Practicalities:
- The more people that can speak, the better.
    - The more recently you have said something, wait for a longer silence before you answer again.
- Please try to avoid too long answers so that we can hear more.
- "RSE" defined broadly.
    - Actually I prefer the term "research engineer", "RSE" is too narrow for my purposes.
    - If you support research processes/data/etc, good.
    - Tell us the *different* things.

Goals:
- Hear about different career paths (beyond traditional academic)
- Hear about our organizations
- Help people find the path that works for them.


## Background

* Do you consider yourself a RE-type person
    * yes: oooooooooooooooooooooo
    * no: o

* Highest (academic) degree?
   * No degree: 
   * BSc: o
   * MSc: oooooo 
   * PhD: oooooooooooooooooo
   * ...

* Do you identify more with
    * R: ooooooo
    * S: ooooooo
    * E: ooooo
    * RE equally: oo0
    * I don't know: ooooo

* Broadly speaking, what's your background?
    - Straight from CS Masters
    - Numerical plasma physics
    - Physics (B.Sc.) -> Biophysics (M.Sc.) -> ML (PhD)
    - Software engineering (B.Sc. + M.Sc), then Condensed Matter Physics (DPhil) -> Research Engineer
    - Physical chemistry --> Computational Chemistry
    - Computational physics
    - Computational physics -> RSE
    - Civil engineering
    - CFD researcher turned Programmer turned HPC specialist
    - Bioinformatics/molecular biotechnology
    - Software Engineering -> Computational Biology -> Bioimaging -> "bioimage RSE"
    - Biomedical engineering (B.Sc. + M.Sc.) + neuroscience (D.Sc.)
    - Researcher in microbiology
    - Geoinformatics / Remote Sensing 
    - Industry/startups/contracting
    - PhD in computational physics -> RSE-like
    - Quantum Technology/Material Science/Computational Physics
    - PhD in Physics -> Chemistry -> Computer Science;
    - CS Masters -> IT department of university library -> RSE

* What did you study?
    - Computer Science, Visualization, and Computer Graphics (nominal amount of Chemistry and Physics – hated it)
    - physics +6
    - mech. engineering o
    - computer science
    - Software Engineering, Complex Adaptive Systems, Physics
    - Biochemistry
    - Oceanography
    - Bioinformatics/molecular biotechnology
    - microbiology
    - computer science
    - biomedical engineering
    - Software & Information Engineering (school, BSc), Computational Biology & Bioinformatics (MSc)
    - inorg chem
    - CS + Linguistics

* When did you get interested in computing
    - I never really ever cared for the computing: I want to solve problems and the world has sent a lot of data and analysis-shaped problems my way.
    - Always was, but really started during my PhD
    - Bachelors - biology was hard and this was more fun
    - high school
    - During PhD, because it felt stupid to click the same things one after another, so I learned about different ways to do that
    - First graded programming project as part of introduction course to programming methods. 
    - during my PhD
    - childhood
    - just before I started studying it
    - During high school through video games
    - College studies to process tons of data

* What were your first projects when you thought "using computing for science is pretty cool?"
    - Game of Life simulation and "Poängpoker" game in command line
    - Writing a bash script to save my PhD housemate several weeks of repetitive labour.
    - HPC adapted hyperparameter search
    - Create a robot in school
    - Lava simulations; although not my core research area
    - It was actually the other way round: "why aren't they?"
    - Predator-pray relationship in ecology (written in Pascal)

* When did you become a "computing person"?
    - I inherited a data collection and analysis pipeline from a more senior PhD student and took it upon myself to refactor it and rewrite it.
    - When I decided to continue with a CS programme after an initial year of not choosing a programme at Uni

Comments from audience during session (started 20 min late :/): 
> - Management processes going against RSE exist ("microsoft university")
> - Sweden: Term RSE not seen outside from here: But shift towards alternative academic careers
> - Funding of RSE-like positions often unclear ("software developers costs as much as professors")
> - Pro-tip: Consider using your experience from PhD and postdoc as X years in data science, machine learning (even if its "just" least squares regression) etc. 
> - Industry seems hesitant to hire people from academia, wondering about group work experience, need to first think about things before proceeding to action


## Current

* What's your HR job title?
    * Digital Research Engineer
    * Uber data scientist (not kidding, överdatascientist in Swedish)
    * application expert
    * Coordinator of open software and AI
    * Planetary Computing Fellow (also not kidding :)
    * Machine Learning Specialist
    * Hired as HPC specialist, but officially a Researcher 
    * Research Engineer, Workplace Manager? (Arbetsledare)
    * Research Engineer +2
    * Application Scientist
    * HPC engineer
    * Bioinformatician
    * Data scientist
    * Research Software Engineer :)

* What do you do?
    * I work in a small team in a hospital that does two things: helps researchers get started with advanced analytics (mainly ML, this is the bit I consider to be RSE work) and builds tools for clinicians to use when they work in the hospital. 
    * I help ecologists save the planet
    * Provide visualization support to researchers in the nation
    * I do the same ^ (i.e. visualisation support to researchers, in case this gets moved)
    * Help researchers with coding and running their code, and longer projects with software design and publishing
    * Define FAIR/open guidelines for developers/researchers, communicate/coordinate projects with other open science initiatives. 
    * Cleanup and maintain softwares 
    * First level scientific HPC user support, support research projects as embedded project member
    * I support biologists with analyzing their images aka consultations, developing reusable workflows, custom analyses, tool development
    * system administration and some development
    * training coordination & development & suppport (related to FAIR research software, FAIR training), community management 

* How did you get this job?  How was it evaluated?  What were the interview(s) like?
    * Network link +1+1
    * "Groomed" by my future colleagues, when they discovered I had no concrete plans after graduating
    * MSc supervisor asked me to apply
    * Did an Erasmus internship and got recruited back into the group 10 years later
    * Found in list of vacancies on university website. Straightforward interview, where I talked about my PhD work which had relevant experience.
    * A colleague was forming a new RSE team at their university
    * Applied for a job that seemed perfect but I thought I wasn't really competent to do, but it turned out to be unexpectedly great match
    * someone knew someone
    * Found out the job posting and applied. Two interview rounds - one with line manager and one with team lead (one fully behavioral interview, one where I was asked to discuss a technical project that I had completed.)
    * Applied for a job that originally seemed very fitting, in the end it was not, but I made it into what I wanted to do (slow process with ups and downs)

* What are your day to day tasks?
    * email. so much email. then some supervision of juniors/trainees/students and coding. a lot of managing up & trying to impact the organisation to make smart choices when it comes to data and AI.
    * Grant writing (and occasionally some science)
    * Mostly coding and some project management.
    * Coordinating my local workplace colleagues, managing the organisation's ticketing system, developing prototypes for users contacting us, developing my skills in personal projects
    * Implementing method for ecology groups
    * Learning a bit about the science of the field I'm helping
    * Plumbing for leaky pipelines
    * data and project "archaeology", sysadmin tasks, backend development, teaching once per term, ...
    * Answering researcher questions in a garage meeting. Maintaining a server. Writing code and documentation.
    * Sys-admin for our servers +2
    * Mentorship to students
    * zoom, teams and chat

* Who works in your team?
    * Professor, assistant professor, associate professor, Postdoc, other research engineer, administrators
    * Research engineers (cluster admins, data scientists, ML pipeline developers, server admins)
    * I am my team :/
    * Our office concists of research engineers (and adjacent to us are also server admins, librarians, and other research support functions). Visualisation projects include researchers in the team.
    * Researchers and research engineers
    * We're a team of 5 data scientists
    * Prof, a couple of fellows, PhD students
    * people do very different things: HPC resource allocation, training, company support, research support, internal development projects, HPC software installations, outreach about research IT services.
    * Different flavours of open science coordinators.
    * 22 researchers and 12 research engineers
    * I'm working "in" a team, but not "as" a team

* How is your position funded?
    * Hospital funds the team from their basic funding, the researchers we support also pay something for the service 
    * Donor funded
    * National funding agency + matching co-finance from workplace
    * partly by university (basic funding), partly by researchers from their project funding +1
    * I'm not asking too many questions
        * I LOVE IT
    * national research infrastructure +1
    * AI-factory and Elixir 

* What is the position of your team in your organisation?
    * We're a "competence centre" within the hospital
    * we're a "competence center"
    * One out of 8 geographical "nodes"
    * Within the infrastructure (InfraVis), one of 8 nodes. Within the university, we are a section (E-commons) focused on research support organised within the Physics division.
    * In one of multiple schools, but partly funded by general IT service
    * one of many groups doing various things

* How is success defined for your role? (publications? delivery?)
    * For my employer publications - for me feeling I contributed
    * I don't know. Making researchers happy. Getting funded.
    * Keeping the data flowing and the servers up
    * Uncertain, not publications though, maybe user satisfaction
    * I don't know. I've been asking for 4+ year and still no answer - I think the organisation doesn't know either.
    * happy customers (researchers) and good course feedback, improved computing environments, up-to-date documentation

* Who has ever written a software management plan (SMP)?
    * Yes:
    * No: oooooo
    * Don't know what it is: o
    * No, but I tell others to: o


## Future

* What do you know now, that you wish someone had told you long ago?
    * Be picky with which projects to engage in – Choose those which deepens skills already being fostered. +1
    * Know when to step away/hand-over
    * You can have influence on what your job looks like, it might not be as easy as just asking, but it can all start with that
    * Some researchers are delightful to work with, many more not so much. If you have any control over it, do not work with the latter group. It will make your life difficult for no real benefit (to you). 
    * That RSE exists
    * RSE career path will exist and it's fine to focus on it.
    * Use git
    * How political academia is
    * Plan B doesn't have to be something you have studied - pick a skill and get better at it

* What do you think is the sustainable balance between academic positions and research engineer positions?
    - A balance that fosters a healthy one-to-many relation between REs and academics, respectively.
    - I see RSEs as being comparable with e.g. lab managers/fieldwork specialists

* What job do you think you might want to go to next? Do you see a clear career path from where you are now?
    - I think I'll stay for the forseeable future – I enjoy the worklife balance I have that I *know* I wouldn't get otherwise.
    - I am happy with my current position for now. There is a "Senior Research Engineer" position that I guess is a possible career path, but it is not as clear as the path towards tenure in academia.
    - I'm looking to move to a more industry-based position to have a slightly higher pace of things happening
    - I don't know
    - Being idealistic but hands-on, how can one "prevent" ending up in a management position?
        - I think this requires actively counteracting. Management creeps up on you if you're competent enough and stay in the same place for long enough unless you fight it.
    - A teaching position could be interesting

* What skills will be more in demand in the future?
    - Hard to foresee, but a "meta" skill in relation to this would be 'adaptability'
    - Being able to smartly utilize support tools (eg LLM based) where it does not impact quality + can help you go faster (e.g. in coding)

* CVs for RSE/Data Scientist jobs
    * I look for software and project management experience, so github or other repos help. Teaching experience or especially mentoring. Communication is an important part of the job. 
    * dig out the position relevant angles in your experience and clearly highlight them

* What is the role of a recruiter in hiring an RSE?
    - Recognise a potential hire's "fitness" for the position, *both* in terms of domain skills and organisational interoperability. 




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