Bioinformatician
Runs and adapts analysis pipelines for research groups or a core facility: QC, alignment, quantification, statistics and clear reports for scientists.
Key skills: Linux, R or Python, NGS workflows, statistics, communication.
Careers guide
Turn your skills into a bioinformatics career.
What the main roles involve, the skills employers look for, how to build a portfolio that gets noticed, and how to prepare for interviews.
Roles
Where bioinformaticians work.
Titles vary between employers, so read the responsibilities, not just the job title.
Computational biologist
Asks and answers biological questions with data. Usually research-focused, often after a PhD, designing analyses and new methods.
Key skills: statistics, modelling, a biological domain, scientific writing.
Bioinformatics engineer
Builds and maintains reliable, scalable pipelines, tools and data platforms used by other scientists.
Key skills: software engineering, testing, workflow managers, containers, cloud.
Clinical bioinformatician
Supports diagnostic genomic testing in healthcare laboratories, where pipelines must be validated, documented and quality-controlled.
Key skills: variant calling and interpretation, validation, quality systems. In the UK, routes include the NHS Scientist Training Programme.
Data scientist in biotech or pharma
Applies statistics and machine learning to discovery, translational or clinical data, often across omics, imaging and clinical records.
Key skills: machine learning, experimental design, Python, communicating with non-specialists.
Research software engineer
Writes and maintains research software, from analysis libraries to web tools, alongside scientists in academia or institutes.
Key skills: software design, version control, testing, documentation, user support.
Skills
What employers look for, and where to learn it.
| Skill | Why it matters | Learn it here |
|---|---|---|
| Linux and the command line | Most bioinformatics software and servers run on Linux. | Level 2 · Linux cheat sheet |
| Python and/or R | For analysis, statistics, plots and automation. | Level 2 · Python · R |
| Version control (Git) | Expected in every team; your GitHub is part of your CV. | Git cheat sheet |
| Sequencing data and file formats | The daily raw material: FASTQ, BAM, VCF, BED and GTF. | Level 3 · File formats guide |
| Statistics and experimental design | Separates trustworthy results from noise. | Level 1 · Level 4 |
| Reproducible workflows | Workflow managers, environments and containers make work dependable. | Level 5 · Containers |
| Machine learning | Increasingly common, and easy to get wrong without care. | Level 6 |
| Communication | Explaining results, uncertainty and limitations to scientists and managers. | Write up every project, and present your work whenever you can. |
Portfolio
Show how you think, not just what you ran.
Two or three well-documented projects beat ten half-finished ones. Each level on Quanexas ends with a portfolio project you can build on.
See example case studies- A clear question. State what you set out to learn and why it matters.
- Data provenance. Give accessions, versions and access conditions.
- Reproducibility. Include an environment file, a README and the exact commands to rerun.
- Sound methods. Explain each decision, including the alternatives you rejected.
- Validation. Use appropriate metrics, baselines and sanity checks.
- Honest limitations. Say what the result cannot show.
- Readable results. Clean figures with labelled axes and short interpretations.
- Tests. Even a few tests show you care about correctness.
CV and applications
Make every line earn its place.
Lead with evidence
“Built a Snakemake RNA-seq pipeline for 48 samples, cutting rerun time from days to hours” says more than a list of tools.
Link your work
Put your GitHub and portfolio at the top, and make sure the pinned repositories have clear READMEs.
Tailor each application
Mirror the job description’s key requirements with your strongest matching examples.
Keep it short
Two pages for most industry roles. Academic CVs can be longer when they list publications.
Explain your biology
Show that you understand the questions behind the data, not only the software.
Ask for feedback
Ask a mentor, a careers service or a community such as Biostars or r/bioinformatics to review your CV.
Interviews
Questions to prepare for.
Open each question to see what a strong answer covers. Practise saying your answers out loud.
Walk me through a project you are proud of.
The question, the data and where it came from, the key decisions and why you made them, how you validated the results, what went wrong and how you fixed it, and the limitations. Keep it to about three minutes, then let them ask questions.
How would you analyse a bulk RNA-seq experiment from raw reads?
Check the design and metadata first; run QC (FastQC, MultiQC) and trim if needed; align (STAR or HISAT2) or quantify (Salmon); build a count matrix; explore samples with PCA; test with DESeq2 or edgeR on raw counts using an appropriate design formula; adjust p-values; interpret effect sizes and enrichment with caution; record versions.
What is the difference between a p-value and a q-value?
A p-value is the probability of a result at least this extreme if there were no true effect. A q-value is adjusted for multiple testing: it is the smallest false discovery rate at which the test would be called significant.
How do you know an alignment worked well?
Mapping rate and properly paired rate (samtools flagstat), duplicate rate, insert-size distribution, coverage and its evenness, MAPQ distribution, and comparison with expectations for the organism and library type. Then look at a few regions in IGV.
Why should you not use TPM values as input to DESeq2?
DESeq2 models raw counts with a negative binomial distribution and estimates its own size factors. TPM values are already normalised and lose the count information that the model relies on.
How would you detect and handle a batch effect?
Look for samples clustering by processing date, kit or lane in PCA. Prevent it with a balanced design; when batches are not confounded with condition, include batch in the model (for example ~ batch + condition). Correction cannot rescue a design in which batch and condition are completely confounded.
How do you make an analysis reproducible?
Version control, pinned environments or containers, a workflow manager, recorded data accessions and parameters, fixed random seeds, tests, and a README that someone else can follow from scratch.
A model shows 95% accuracy. What do you ask next?
What is the class balance and the majority-class baseline? How was the data split, and could anything have leaked from test to training? What are recall, precision and balanced accuracy? Has it been tested on independent data?
Coding task: count the records and total length in a FASTA file.
Read line by line; count lines starting with ">" as records and add up the lengths of the other lines. Mention edge cases (empty lines, wrapped sequences, Windows line endings), memory use for large files, and that Biopython’s SeqIO or seqkit stats would be the production choice.
Tell me about a time an analysis went wrong.
Choose a real example. Explain how you noticed the problem, how you found the cause, what you changed to fix it and prevent it happening again, and what you learned. Honesty and ownership matter more than a perfect story.
Where to look
Jobs, PhDs and fellowships.
Search several places, set alerts, and look at the careers pages of research institutes and companies you admire.
Nature CareersScience jobs worldwide, academic and industry.
Science CareersJobs and career advice from the journal Science.
jobs.ac.ukUK academic, research and university jobs.
EMBL jobsIncluding EMBL-EBI, a major bioinformatics employer.
NHS JobsClinical bioinformatics and genomics roles in the UK.
EURAXESSResearch jobs and fellowships across Europe.
FindAPhDPhD projects, many of them funded.
LinkedIn JobsIndustry roles, plus a place to share your projects.
ISCBConferences, student groups and a careers community.
Build the evidence
Every level ends in a portfolio project.
Work through the levels and you will finish with projects that show employers exactly how you think.