Bioinformatics CV Example
A bioinformatics CV bridges computational science and biology, showcasing your ability to analyse large-scale biological datasets.
Recommended template: Compact
Key Skills to Include
Quick Tips
- Emphasise programming languages and bioinformatics tools you are experienced with.
- Highlight any pipelines or software tools you have developed.
- Include links to your GitHub profile or publicly available code repositories.
- Demonstrate your ability to communicate complex computational findings to biologists.
Ready to build your CV?
Start with the Compact template and customise it for your science role.
How to Write Your Bioinformatics CV
A bioinformatics CV must demonstrate your dual competency in computational science and biological understanding. Hiring managers look for evidence that you can write robust code, manage complex datasets, and translate computational outputs into biologically meaningful insights. Whether you are targeting academic, clinical, or industry roles, your CV should show practical pipeline-building experience alongside domain-specific knowledge in genomics, proteomics, or related fields.
CV Structure
Use a reverse-chronological format with clear sections for your profile, experience, education, technical skills, and publications or software contributions. Each role should describe the project context, the data types you worked with, and the tools you used. Follow with quantified achievements such as publications, pipeline performance improvements, or diagnostic discoveries. Keep the CV to two pages, with a separate GitHub link or portfolio for code samples.
CV Format
Choose a clean, modern template that reflects the technical nature of your work. Use monospace font for tool names or code references if it aids readability. Ensure consistent formatting for dates and section headings. Provide hyperlinks to your GitHub profile, ORCID, or published software tools directly within the CV.
CV Profile Examples
Senior Bioinformatician
Experienced bioinformatician with eight years of expertise in genomics, transcriptomics, and pipeline development for clinical and research applications. Proficient in Python, R, and Nextflow, with a proven ability to deliver scalable analysis solutions for next-generation sequencing data. Lead author on six publications and a regular contributor to open-source bioinformatics tools on GitHub.
Clinical Bioinformatician
HCPC-registered clinical bioinformatician working within an NHS Genomic Medicine Service, specialising in variant calling, annotation, and interpretation for rare disease and cancer genomics panels. Experienced in maintaining validated bioinformatics pipelines under ISO 15189 accreditation standards. Committed to improving diagnostic yield through continuous pipeline optimisation.
Research Bioinformatician
Computational biologist with a PhD in systems biology and three years of postdoctoral experience analysing multi-omics datasets in the context of neurodegenerative disease. Skilled in single-cell RNA sequencing analysis, network inference, and machine learning classification. Eager to apply advanced analytical methods to high-impact translational research questions.
State your specialism (genomics, transcriptomics, clinical bioinformatics), years of experience, core languages, and a headline achievement. Mention the data types and scale you typically work with to give context to your expertise.
Key Skills for Your Bioinformatics CV
Python
Writing scripts and packages for data processing, statistical analysis, and pipeline development using libraries such as Pandas, NumPy, and Biopython.
R Programming
Conducting statistical analysis and producing publication-quality visualisations using tidyverse, Bioconductor, and ggplot2 packages.
Genomic Data Analysis
Processing whole genome, exome, and targeted sequencing data from raw reads through alignment, variant calling, and annotation.
Machine Learning
Applying supervised and unsupervised learning methods to biological datasets for classification, clustering, and feature selection.
Sequence Alignment
Aligning DNA and protein sequences using tools such as BWA, STAR, Bowtie2, and BLAST to map reads or identify homologues.
Database Management
Querying and maintaining biological databases using SQL, MongoDB, or custom REST APIs for data integration and retrieval.
Statistical Modelling
Fitting regression, mixed-effects, and Bayesian models to experimental data to test biological hypotheses rigorously.
Next-Generation Sequencing
Understanding Illumina, Oxford Nanopore, and PacBio platforms including library preparation concepts, run metrics, and data quality assessment.
Pipeline Development
Building reproducible, scalable analysis workflows using Nextflow, Snakemake, or WDL for high-throughput data processing.
Work Experience Examples
Describe the biological question, the data type and scale, the tools you used, and the outcome. Bioinformatics roles often involve building things — pipelines, databases, visualisation tools — so describe what you built, how it performed, and who used it. Quantify runtime improvements, diagnostic yields, or adoption metrics where possible.
Senior Bioinformatician
Genomics England, London
Developed and maintained production-grade bioinformatics pipelines for the 100,000 Genomes Project, supporting clinical interpretation of whole genome sequencing data.
Responsibilities
- Built and optimised variant calling and annotation pipelines using Nextflow, Docker, and cloud-based HPC infrastructure on AWS.
- Performed quality control analysis on incoming sequencing data, flagging samples that failed coverage or contamination thresholds.
- Collaborated with clinical scientists to refine gene panel content and variant filtering strategies for rare disease diagnostics.
- Developed Python and R scripts for automated reporting, data visualisation, and integration with the Genomics England research environment.
- Contributed to internal code reviews and maintained pipeline documentation to support reproducibility and audit compliance.
Achievements
- Reduced variant calling runtime by 50% through pipeline parallelisation and container optimisation, enabling faster turnaround for clinical reports.
- Designed a novel structural variant detection module that identified pathogenic copy number variants in 12 previously undiagnosed rare disease families.
- Authored two peer-reviewed publications on bioinformatics methods for clinical genomics in Genome Medicine.
Bioinformatics Analyst
Wellcome Sanger Institute, Hinxton
Analysed large-scale single-cell RNA sequencing datasets as part of the Human Cell Atlas consortium, mapping gene expression across human tissues.
Responsibilities
- Processed raw scRNA-seq data using Cell Ranger and Scanpy, performing quality filtering, normalisation, and dimensionality reduction.
- Applied clustering algorithms and differential expression analysis to identify novel cell subtypes across multiple tissue types.
- Maintained reproducible analysis workflows using Jupyter notebooks and Snakemake pipelines on the institute's HPC cluster.
- Presented analysis results at weekly team meetings and contributed figures and methods text for consortium publications.
Achievements
- Identified a previously uncharacterised immune cell population in human gut tissue, contributing to a Nature paper with over 300 citations.
- Developed a reusable Snakemake workflow for scRNA-seq preprocessing that was adopted by three other teams at the institute.
Education & Qualifications
List your highest qualification first, including thesis title and supervisor if relevant. Bioinformatics draws from multiple disciplines, so highlight coursework or training in both computational and biological sciences. Include any online certifications in cloud computing, machine learning, or specialised bioinformatics platforms.
PhD in Bioinformatics or Computational Biology
Doctoral qualification demonstrating the ability to develop and apply computational methods to answer biological questions.
HCPC Clinical Scientist Registration
Professional registration required for bioinformaticians working in NHS diagnostic genomics laboratories.
AWS Cloud Practitioner / Solutions Architect
Cloud computing certification increasingly valued for bioinformaticians working with large-scale genomics data on cloud infrastructure.
RSB Membership (MRSB)
Membership of the Royal Society of Biology indicating professional standing within the life sciences community.
Frequently Asked Questions
What programming languages should I list on a bioinformatics CV?
Should I include a GitHub link on my bioinformatics CV?
How do I show biological knowledge on a computational CV?
How long should a bioinformatics CV be?
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