Bioinformatics CV example

Writing code to process genomic data? Sure. Visualising protein networks across species? No problem. Writing a CV that captures all of that in two pages or less? Slightly less intuitive.

This guide will help you craft your bioinformatics CV with clarity, and – just as importantly – tailor your experience and skills to reflect the demands of hiring managers across research and healthcare settings.

Bioinformatics sits at the intersection of biology and statistics – and your CV should reflect that same clarity and structure. A well-organised layout makes it easy for recruiters to scan through your academic qualifications and project experience without confusion and mistakes. 

Here’s the layout to follow:

• Name and contact details – Start with your name and personal details – make it simple for recruiters to reach you. Including a photo is a personal choice.
• Profile – Use this section to summarise your experience, strengths, and what makes you a standout candidate.
• Core skills – Outline your primary competencies to give recruiters a snapshot of your strengths.
• Work experience – Walk through your professional experience, beginning with your latest position and moving backwards.
• Education – Outline your education and certifications, focusing on those most relevant to the role.
• Additional info – This section is optional, but it’s a good place for hobbies and interests that complement your CV.
  

To keep your CV sharp and readable, use a clean, academic-appropriate font and break up the content with clear section headings and bullet points in each section. Stick to two pages length max, use white space strategically, and avoid unnecessary graphics or formatting. Treat your CV format like a well-documented script – straightforward and precise.

This section is your CV’s headline – a quick summary of why you’re the kind of bioinformatician who adds measurable value to research or development teams. It’s not just about where you studied or what you know, but what you bring to the table: technical depth, cross-disciplinary insight, and the ability to solve biological problems with code.

Recruiters want to understand how your work translates into real-world outcomes. So focus your CV profile on the benefit you’d bring to anyone who takes you on board.

To show your value clearly, include:

• Where you worked – Academic research groups, pharma R&D teams, genomics labs, hospitals
• Top qualifications – MSc or PhD in Bioinformatics, Systems Biology, Computational Biology
• Key strengths – Large-scale data analysis, pipeline automation, statistical modelling
• Specialisms – Genomics, transcriptomics, metagenomics, structural bioinformatics, etc.
• Tools and outcomes – e.g. using Python to optimise RNA-seq workflows or helping translate results into actionable diagnostics
  

When recruiters scan your CV, they’re looking for clear evidence of technical ability. This section should focus on hard, tangible CV skills – tools, languages, and domain-specific expertise – not vague phrases.

Mention your proficiency with programming languages, and reference your understanding of statistical techniques, cloud computing for data handling, or experience in creating reproducible pipelines.

This short list is your best chance to align yourself with the job spec – so make sure your listed skills are targeted, recent, and immediately recognisable to employers in the bioinformatics space.

This section proves that you can put your academic and technical knowledge into real-world practice. Whether your background is in academia, healthcare, or industry, employers want to see how you applied your skills and contributed to successful projects.

List your work experience in reverse chronological order. For each position, begin with a short overview of the lab, company, or research focus. Then use bullet points to outline what you did and what impact it had – for instance, achievements like improving the speed of a variant-calling pipeline or helping validate biomarkers in clinical datasets.

Don’t shy away from referencing collaboration: bioinformatics rarely happens in isolation, and your ability to work with biologists, data scientists, or clinicians matters.

• Outline – Briefly describe the lab, research group, or company, what kind of work it focused on, and your position in the team.
• Responsibilities – Use action words like “analysed” and “developed.” For example: “analysed RNA-seq data using custom R scripts and Bioconductor packages” or “developed automated pipelines for WGS data analysis using Snakemake.” Mention tools, methods, and data types where relevant.
• Achievements – Highlight your impact: reduced processing time, improved reproducibility, published results, or contributions to high-impact studies. Use data or outcomes if available.

Education is a major component of your CV in this field – especially if you’re working in research or applying to academic positions. Start with your most recent qualification, typically an MSc or PhD in a relevant subject, followed by your undergraduate degree.

Mention your degree discipline, institution, and (optionally) your dissertation or thesis focus if it’s directly relevant to the role. You can also reference specific modules or research placements if you’re early in your career.