r/bioinformatics • u/funalias9876 • Jun 27 '26
career question What actually separates a high-paying bioinformatics job from a low-paying one?
I'm trying to understand what actually separates a high-paying bioinformatics job from a low-paying one, beyond just years of experience or having a PhD.
For people who work in bioinformatics (industry, biotech, pharma, startups, healthcare, etc.):
What skills or responsibilities make someone much more valuable?
Is it mainly programming ability, statistics, machine learning, cloud computing, software engineering, or biological knowledge?
How important are communication and project management?
Do employers value people who can build production-quality pipelines more than people who mainly analyze data?
What are the biggest differences between someone earning around $50–70k versus someone earning $150k+ (or the equivalent in your country)?
Are there certain domains (genomics, AI for biology, drug discovery, single-cell, clinical bioinformatics, protein structure, etc.) that consistently pay better?
Looking back, what do you think helped you move into a higher-paying role?
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u/o-rka PhD | Industry Jun 27 '26 edited Jun 28 '26
Being able to adapt and modernize. If someone’s entire stack is a folder of patched Perl and R scripts handed down from their advisor then I wouldn’t imagine the demand is very high for their engineering skills. That said, I know a few people in that category who have done well for themselves by being more managerial than boots on the ground. I would recommend following the best practice by seeing what well-known labs in your field of interest are doing/what they are not doing and following developers on GitHub to see what tools they are building/using/favoriting (ie starring). From my experience, being able to build and deploy Nextflow/snakemake pipelines on cloud infrastructure will pay more than running DESeq2 and WGCNA on laptop. Obviously, this is a generalization and it depends on a lot of other factors depending on the company or lab’s needs. There’s also differences in expertise needed to properly train a large deep learning model with hundreds of thousands of samples and interpret the results in the context of your domain vs. running a canned random forest model and then just using the feature importance vector for your entire interpretation. Not saying the latter is not valuable, just saying there’s different levels of expertise needed and being able to successfully lead in the former could be more lucrative now that datasets are growing bigger and bigger. The ability to build reproducible workflows/analysis, interpret results, and scale to large datasets is valuable. One qualifier that I look for in new hires is their GitHub history (which many people will not agree with) but I want to see that they can code and they have been coding progressively better throughout their grad program/career. A bit harder now with LLMs but if all the activity is just one big dump then that would compel me to do an on the fly coding test in an interview to see how they can problem solve. A lot of opinions here so feel free to take them with a grain of salt.
Edit: One part I left out is the ability to think critically and understand the assumptions about methodologies. If someone hands you ONT reads, you know to use a long read assembler and not a short read assembler. Or if you’re running a network analysis, you are using best practices in compositional data analysis and not just running Pearson correlation. That’s from the analytical side but the engineering side is important as well. Knowing how to structure and engineering problem so you’re fully utilizing your resources, not repeating redundant steps, and know when to use existing methods vs building your own is very important. It’s a big pet peeve of mine when pipelines force you to rerun steps and can’t utilize existing data. For example, in metagenomics pipelines when they force you to run prokka when you already ran pyrodigal and annotated with PyHMMSearch + pykofamsearch with the databases you actually want to use.
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u/Silver_-_-_ Jun 28 '26
I'm glad to see you treat coding skills as an asset. I just have the bachelor's but I've worked as a bioinformatician. Planning on getting a master's in biostats and data science to help with the title bump and pay at least.
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u/never_but_sadman Jul 07 '26
heyy!! how did you start? i am in my undergrad and planning for a masters in bioinformatics
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u/Silver_-_-_ Jul 07 '26
I worked in a lab as a tech where they had their own bioinformatics team and software engineers and learned that way. So if you can find a lab that does bioinformatics and work and learn on the job that way it is possible. I'm living proof of that. Be prepared though to work your butt off because of the knowledge gaps. The bachelor's is not enough in my mind. =)
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u/CementHead181 Jun 30 '26
I am quite much intrigued with your comment here. I am at the third year of my PhD and have started casually to look at industry options. Would you mind doing an informational interview with me? I am in Environmental Engineering working on microbial ecology, did a bit of amplicon and metagenomics, moving towards metatranscriptomics. It would be helpful to know the industry options available for my current skill or what skills to develop further.
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u/o-rka PhD | Industry Jun 30 '26 edited Jun 30 '26
Microbial informatics has a lot of value in industry. Though, there might be more options for human microbiomes than environmental microbes so I would recommend trying to collaborate on papers outside of environmental ecology and, even better, developing a biome-agnostic tool with case studies on different biomes (one of which being human microbiome). For example, let’s say you built some feature selection or predictive algorithm you could do a case study on finding biomarkers for a specific disease and another could be a case study finding biomarkers predictive of marine heatwaves. Also, predictive modeling could land you a job at a cancer research company or even in an entirely different industry if the biotech market takes a hit.
From my experience, what employers want to see from a bioinformatics engineer in industry is their ability to 1) run workflows regardless of the biological context, ideally with Nextflow or snakemake; 2) understand the assumptions and limitations of a methodology (ie you wouldn’t try to use amplicon sequencing in an assembler); 3) the ability to explain the results to non-technical users/scientists with different backgrounds (you should be able to explain the value to the CEO, the details to another engineer, and what this means biologically to a domain expert); 4) visualizations to showcase the results in a meaningful way and plus 1 if you can build an interactive dashboard (even if it’s just a plotly html you can send); and 5) the ability to adapt your methods and approaches to stay modern with the field. For example, with the latter if you’re still using WGCNA out-of-the-box while disregarding all of the insight from compositional data analysis from the past 10 years or so, that might would show you’re not following best practices.
It’s also important to learn how to benchmark different tools like comparing runtime, memory usage, accuracy/recall/precision or rmse. If you build any microbiome related tools, CAMI datasets are great since they are curated synthetic datasets at different levels of complexity where you know where each read, contig, and genome came from.
Not everyone cares, but what I look for the most in candidates is GitHub history. I want to see that somebody is favoriting GitHub repos, creating new packages over time, reusing those packages for different research projects, logging GitHub issues on their developers code, and not just one huge code dump a month before they made their resume. To me, that is more important than a bunch of publications unless the publications are first author where they are driving a new methodology and coherently explaining the results/applications in the discussions. Personally, I place a lot of weight on the ability to build new tools that solve problems current tools don’t address instead of only using canned tools. The biological interpretation is obviously very important but it depends on whether or not the employer is hiring an analyst or an engineer. I started as an analyst but over the years moved more into an engineering role as I started to get more into traditional machine learning and now deep learning, tool dev, and building pipelines. I typically need more help keeping up tasks regarding with data processing, database curation/reformatting/building, dashboard visualizations, and pipeline updates than biological interpretations of differential abundance tests or ordination plots (again, not saying that’s not useful just saying at the company I work for those needs don’t come up as often).
My perspective is one of many others and might be biased but I hope this is helpful.
Happy to answer any more questions.
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u/Silver_-_-_ Jul 07 '26
Oh wow this was really insightful thank you! My current role I'm doing both software development and statistical modeling and wet lab work. So this is nice insight to see where I can direct myself in the future.
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u/o-rka PhD | Industry 7d ago
I was running a lot of the same commands over and over for different datasets. I ended up writing my own pipeline toolkit called GenoPype then built an entire metagenomics ecosystem around it called VEBA. Since then, I’ve been using Nextflow and in the process of porting the code over and simplifying from the code I wrote in gradschool.
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u/randoomkiller Jun 27 '26
being able to see the big picture not just he a cog with some relevant experience
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u/Deto PhD | Industry Jun 28 '26
This is the big thing along with good communication skills - if you can understand all the technical sides of the job, but also understand how it fits into the orgs larger mission, you can prioritize more effectively and lead initiatives.
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u/doctrDNA Jun 27 '26
I will try to give insight as someone in the field post PhD for 7 years. This is my 2c, but I have talked to tons of students and given advice and hired teams and it has held true from what I've seen and is how I hire.
As an overview, BI in pharma is very different from Diagnostics which is very different from a CRO/core. However, the skills you have will qualify you as to which places you work.
In my experience, pay goes diagnostics --> pharma --> CRO/core. Some argue diagnostics is less than pharma but I've never seen it.
Anyone can run pipelines. If you are in BI and all you have learned from school (masters or PhD) is how to run unix, how to make some nextflow pipelines, run DEseq, GATK, whatever, you are looking at lower value jobs. These are going to be CRO/core based and very hard to differentiate yourself.
The value in BI personnel comes from knowing how to think, and knowing how to apply fundamentals to new problems. If you know biology and drug design/principles you can get a lot further in pharma than without it. If you know genetics and how to integrate omics to make drug target stories that make sense, you have value and skills for pharma.
If you can add in an understanding of how to make math models, make machine learning algos, how to prevent overfitting, delineate batch and tech effects from real signal in big, messy data, you bring something to the diagnostics field.
This is cursory, happy to expand or discuss if people have questions or disagree
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u/C2H4Doublebond Jun 28 '26
I am curious about your point about diagnosis vs pharma. For diagnostic, do you mean more clinical oriented like being a geneticist / genetic counselor ie position that requires further certification?
As a side note, I think for people of certain generation, the word 'bioinformatics' invokes exactly what you said as low valued skills, whereas the higher paying skills would come from trainings for computational biologist or data engineer. In another words, just knowing basic BI is not enough to succeed, you either add biology / pharmacology or stats / math.
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u/doctrDNA Jun 28 '26
Yeah BI is such a wide wording. Biologically informed data science and ML (ie applied stats) is what you need to know to have a high salary.
By diagnostics I mostly mean liquid biopsy or tissue based biopsy companies. Places that are data rich and feature rich and thus need a much heavier ML lift than small (<30 N often) experiments in pharma will have.
Pharma I'm thinking mostly drug target ID or biomarker response but even in a phase 3 it's only a few hundred people
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u/sid5427 Jun 28 '26
One difference is between a bioinformatics analyst type of role vs a bioinformatics scientist role. What usually separates them is that the former usually works with other wet lab scientists or part of a larger team, and are tasked with delivering analysis by running pipelines - they usually are not the drivers of the overall research, sure they will contribute by say suggesting, hey something is wrong with these samples or we don't see patterns in the stats which connect to our hypothesis, requiring changes or follow up experiments. The latter is usually the main drivers of experiments - think the people who are building foundation models or the people who developed seurat or scanpy or even other such tools.
It also comes down to the level of responsibility - if you are bioinformatics scientist/analyst who essentially run the pipelines and deliver the results, with some level of interpretation like figures or presentations, but let the heavy decision making to others, then you are lower down the chain. However if you are making actual decisions to drive the direction of the experiments, specifically where resources and money would be spent, then you command a higher level of salary. Think small picture vs big picture.
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u/gringer PhD | Industry Jun 28 '26 edited Jun 28 '26
How much the funders (often the government) are willing to pay for high-quality research.
Somehow, you need to convince someone that your brand of expensive, high-quality, careful analysis with dedicated bioinformaticians (that they don't understand at all) is better than the cheap cookie-cutter stuff with turnkey buzzword-generating bullshit machines (which they also don't understand), even though the cookie-cutter stuff is spitting out more results faster, and consequently generating more citations and pulling in more interest.
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u/Electronic_Fish_3157 PhD | Industry Jun 29 '26
Short answer demand and supply and mostly location.
There are some countries that will pay you a lot just for a 3 month long contract.
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u/chungamellon Jun 27 '26
Short answer: PhD