6,509 Bioinformatician jobs in the United Kingdom

Bioinformatician (C>)

Abercorn Sinclair is delighted to partner with Trogenix, an innovative gene therapy company based in Edinburgh. Following a successful £70m Series A, Trogenix is expanding its informatics team and seeking a talented Bioinformatician to play a key role in advancing discovery and development across its cutting-edge therapeutic platform.

Role Summary

We are seeking a Bioinformatician to join our growing team in Edinburgh, UK. You will develop and apply computational methods to large-scale regulatory genomics datasets, driving the discovery and design of novel gene therapies. The role combines hands-on analysis of high-throughput sequencing data with the development of reproducible pipelines to support our discovery platform. Working closely with wet-lab scientists and cross-functional teams, you will deliver insights that directly influence our scientific direction and therapeutic outcomes.

The candidate

The ideal candidate will be educated to PhD level in bioinformatics, computational biology, genomics, or a related discipline, with relevant industry (preferred) or postdoctoral experience. Candidates with an MSc in a relevant field and considerable experience in industry or research will also be considered.

Role responsibilities

• Develop, maintain, and optimise bioinformatics pipelines using Nextflow (nf-core best practices).
• Analyse and interpret sequencing datasets, including ATAC-seq, ChIP-seq, Cut&Run, RNA-seq, and single-cell assays to advance discovery projects.
• Apply statistical and computational methods in Python and R to extract and visualise actionable insights.
• Integrate multi-omics datasets to characterise enhancer landscapes across human disease, with a primary focus on cancer.
• Write clean, reproducible, and well-documented code, following good software engineering practices (e.g. version control, testing, containerisation).
• Partner with experimental scientists and senior leadership to align analyses with project and translational objectives.
• Stay up to date with developments in bioinformatics methods and integrate relevant approaches into ongoing work.

Required Skills and Experience

• Demonstrated expertise in the analysis of regulatory genomics datasets (ATAC-seq, ChIP-seq, Cut&Run, RNA-seq, single-cell).
• Strong programming skills in Python and R for statistical analysis, workflow development, and data visualisation.
• Hands-on experience with workflow management systems, ideally Nextflow; exposure to Snakemake or similar is advantageous.
• Familiarity with collaborative software development, including version control (Git), code review, CI/CD, testing, and reproducible research practices.
• Strong communication skills with the ability to present results clearly to both technical and non-technical audiences.
• Collaborative mindset and ability to work effectively as part of cross-functional teams, including close interaction with wet-lab scientists.

Desirable Skills

• Experience with cloud computing environments (Azure, AWS, or GCP).
• Exposure to machine learning approaches for biological data.
• Familiarity with containerisation technologies (Docker, Singularity).
• Broader experience across other omics data types (e.g. Hi-C, proteomics).
• Prior experience in a biotech, pharmaceutical, or start-up setting.
• Interest in translational research and contributing to publications or presentations.

Company Values

• We Act with Positivity

We are bold in our ambition and innovation, acknowledge risks, and embrace challenges with enthusiasm.

• We Commit

We are accountable for our goals, act with urgency, and do what we say we will do.

• We Are Transparent

We maintain transparency in every aspect of our business – our goals, planning, data, communication, successes, and challenges.

• We Act with Camaraderie

We are inclusive, empathize, have fun together, trust each other, and always give our best.

The Opportunity

This is an amazing opportunity to join a rapidly growing, oncology-focused gene therapy company driving breakthrough treatments for aggressive solid tumours. You’ll collaborate with world-class experts in gene regulation, synthetic biology, and immunotherapy, contributing directly to the creation of next-generation therapies with the potential to transform patient outcomes.

You can expect a competitive salary and a comprehensive benefits package, including participation in ESOP, critical illness cover, private medical insurance, pension, and cycle-to-work scheme.

How to Apply

For a confidential discussion, please contact our retained partner:

Keith Tocher, Abercorn Sinclair

About Abercorn Sinclair

Abercorn Sinclair is a specialist recruitment partner offering tailored consultancy and advisory services in talent strategy. We provide our clients with the insight and support needed to attract, retain, and develop the skills that drive long-term success.

• Lead the design, development, and implementation of bioinformatics pipelines for the analysis of large-scale biological datasets.
• Analyze genomic, transcriptomic, proteomic, and other omics data to identify biological patterns and generate hypotheses.
• Collaborate with experimental scientists to design studies and interpret results effectively.
• Develop and maintain robust data management strategies for complex biological data.
• Apply statistical and machine learning methods to biological data analysis.
• Supervise and mentor junior bioinformaticians and research staff.
• Contribute to the development of novel algorithms and computational methods.
• Present research findings at scientific conferences and publish in peer-reviewed journals.
• Stay abreast of the latest advancements in bioinformatics tools, technologies, and biological research.
• Ensure data integrity, reproducibility, and compliance with relevant data privacy regulations.

• PhD in Bioinformatics, Computational Biology, Computer Science, Statistics, or a related quantitative discipline.
• Extensive experience in bioinformatics analysis of large-scale omics datasets.
• Proficiency in programming languages such as Python, R, and Bash scripting.
• Experience with relevant bioinformatics tools and databases (e.g., BLAST, ENSEMBL, UCSC Genome Browser).
• Strong understanding of statistical methods and machine learning techniques applied to biological data.
• Proven experience in managing and leading research projects and teams.
• Excellent communication and collaboration skills, with the ability to work effectively in a remote environment.
• Strong publication record in reputable scientific journals.
• Experience with cloud computing platforms (AWS, GCP, Azure) is advantageous.
• A passion for solving complex biological problems through computational approaches.

• Design, develop, and validate robust bioinformatics pipelines for the analysis of next-generation sequencing (NGS) data (e.g., WGS, WES, RNA-Seq, ChIP-Seq).
• Lead the analysis of complex genomic, transcriptomic, and proteomic datasets to identify novel biological insights and drug targets.
• Develop and implement machine learning models for biological data analysis and prediction.
• Collaborate closely with experimental biologists, clinicians, and other researchers to define research questions and analysis strategies.
• Manage and curate large biological databases, ensuring data integrity and accessibility.
• Provide expert bioinformatics support and guidance to research groups across the institute.
• Mentor and supervise junior bioinformaticians and research fellows.
• Contribute to grant proposals and secure funding for bioinformatics research.
• Stay abreast of the latest advancements in bioinformatics, genomics, and computational biology.
• Present research findings at national and international conferences and publish in high-impact journals.
• Ensure the implementation of best practices for computational reproducibility and data management.
• Contribute to the strategic planning of the institute's bioinformatics infrastructure and services.

• Ph.D. in Bioinformatics, Computational Biology, Computer Science, Statistics, or a related quantitative field.
• Extensive experience (8+ years) in bioinformatics, with a strong focus on genomics and NGS data analysis.
• Demonstrated expertise in developing and implementing complex bioinformatics pipelines.
• Proficiency in programming languages such as Python and R, and experience with shell scripting.
• Experience with common bioinformatics tools and databases (e.g., Ensembl, UCSC Genome Browser, GATK).
• Strong understanding of statistical methods and machine learning techniques applied to biological data.
• Proven ability to manage research projects and lead a team.
• Excellent communication, presentation, and interpersonal skills.
• Experience working in a collaborative, multidisciplinary research environment.
• Knowledge of cloud computing platforms (e.g., AWS, GCP) is an advantage.

• Develop, implement, and maintain bioinformatics pipelines for the analysis of high-throughput sequencing data (e.g., RNA-Seq, WGS, ChIP-Seq).
• Perform statistical analysis and interpretation of genomic, transcriptomic, and proteomic datasets.
• Design and execute computational experiments to identify potential drug targets and biomarkers.
• Collaborate with experimental teams to integrate computational findings with biological hypotheses.
• Develop and utilize machine learning models for predictive analysis and data interpretation.
• Contribute to the design of experiments, advising on data collection strategies and analytical approaches.
• Stay current with the latest advancements in bioinformatics tools, algorithms, and biological databases.
• Present research findings to internal teams and at scientific conferences.
• Contribute to scientific publications and the preparation of grant proposals.
• Ensure the quality, reproducibility, and scalability of bioinformatics analyses.
• Mentor junior bioinformaticians and support the broader computational biology team.
• Manage data resources and ensure data integrity and security.
• Effectively communicate complex computational concepts to non-computational colleagues.

• PhD or Master's degree in Bioinformatics, Computational Biology, Computer Science, Statistics, or a related quantitative field.
• A minimum of 5 years of post-graduate or industry experience in bioinformatics, with a focus on biological data analysis.
• Proficiency in programming languages such as Python or R, and experience with bioinformatics libraries.
• Extensive experience with common bioinformatics tools and databases (e.g., BLAST, Ensembl, UCSC Genome Browser).
• Strong understanding of statistical methods relevant to biological data analysis.
• Experience with cloud computing platforms (e.g., AWS, Google Cloud) is a plus.
• Familiarity with drug discovery processes and preclinical research is desirable.
• Excellent problem-solving skills and a meticulous approach to data analysis.
• Strong communication and collaboration skills, with the ability to work effectively in a remote team setting.
• Proven ability to manage multiple projects and meet deadlines.
• Experience with version control systems (e.g., Git).

• Analyze and interpret large-scale genomic, transcriptomic, and proteomic data.
• Develop and implement bioinformatics pipelines and workflows.
• Apply statistical methods and machine learning to biological data.
• Visualize complex biological data for interpretation and presentation.
• Collaborate with researchers to define data analysis strategies.
• Develop and maintain bioinformatics databases and tools.
• Stay current with advances in bioinformatics and computational biology.
• Contribute to research publications and grant applications.

• PhD or MSc in Bioinformatics, Computational Biology, Statistics, or a related field.
• Minimum 5 years of experience in bioinformatics research.
• Strong programming skills in Python and R.
• Experience with bioinformatics tools and databases (e.g., BLAST, Ensembl, TCGA).
• Proficiency in handling next-generation sequencing (NGS) data.
• Knowledge of statistical modeling and machine learning techniques.
• Experience with high-performance computing environments.
• Excellent analytical, problem-solving, and communication skills.

• Develop and implement robust bioinformatics pipelines for the analysis of large-scale genomic, transcriptomic, and proteomic data.
• Design and execute computational experiments to identify key biological insights and patterns.
• Collaborate with experimental biologists, clinicians, and other researchers to define research questions and provide data analysis support.
• Develop and maintain databases for storing and querying biological data.
• Contribute to the interpretation of complex biological datasets and the generation of publishable results.
• Write clear, concise, and well-documented code for data analysis and pipeline development.
• Stay current with the latest advancements in bioinformatics tools, algorithms, and methodologies.
• Present findings to internal teams and at scientific conferences.
• Mentor junior bioinformaticians and contribute to the training of research staff.
• Ensure data integrity, reproducibility, and adherence to best practices in computational research.

• MSc or PhD in Bioinformatics, Computational Biology, Computer Science, Statistics, or a related quantitative field.
• Significant experience in applying bioinformatics tools and techniques to biological data.
• Proficiency in programming languages such as Python and R, including relevant bioinformatics libraries.
• Experience with high-performance computing (HPC) environments and cloud computing platforms.
• Strong understanding of statistical methods and their application in biological data analysis.
• Familiarity with various sequencing technologies and data types (e.g., WGS, RNA-Seq, ChIP-Seq).
• Experience with database management (e.g., SQL).
• Excellent problem-solving and analytical skills.
• Effective communication and collaboration skills, with the ability to explain complex computational concepts to non-experts.
• A track record of contributing to scientific publications is desirable.

• Designing and implementing computational pipelines for the analysis of diverse biological datasets, particularly NGS data (e.g., RNA-Seq, ChIP-Seq, WGS).
• Performing statistical analysis and interpretation of genomic and proteomic data to identify key biological patterns and associations.
• Developing and applying advanced algorithms for sequence alignment, variant calling, gene expression analysis, and pathway analysis.
• Collaborating with experimental biologists to define research questions and design appropriate bioinformatics strategies.
• Visualising complex biological data in an understandable and informative manner for presentations and publications.
• Maintaining and managing large biological databases and computational resources.
• Troubleshooting and optimising bioinformatics tools and workflows.
• Mentoring junior bioinformaticians and research staff in computational approaches.
• Staying up-to-date with the latest advancements in bioinformatics, computational biology, and relevant life sciences.
• Contributing to manuscript preparation and presentation of research findings at scientific conferences.

• Design, develop, and implement sophisticated bioinformatics pipelines for the analysis of large-scale omics data (genomics, transcriptomics, proteomics, metabolomics).
• Lead and mentor a team of bioinformaticians, providing technical guidance, project oversight, and professional development support.
• Collaborate closely with experimental biologists, clinicians, and other researchers to define research questions and translate them into computational analyses.
• Develop and maintain databases and computational resources for data storage, management, and retrieval.
• Stay at the forefront of bioinformatics research, identifying and integrating novel algorithms, tools, and methodologies.
• Contribute to the interpretation of complex biological data, generating actionable insights and hypotheses.
• Author and contribute to peer-reviewed publications and present research findings at international conferences.
• Ensure the reproducibility and scalability of bioinformatics analyses.
• Manage multiple research projects concurrently, adhering to project timelines and deliverables.
• Foster a collaborative and innovative research environment within the bioinformatics team and across the institution.

• Ph.D. in Bioinformatics, Computational Biology, Computer Science, Statistics, or a related quantitative field.
• A minimum of 7 years of progressive experience in bioinformatics research and analysis, with a proven track record of leadership.
• Extensive experience in analyzing large-scale omics datasets using various bioinformatics tools and statistical methods.
• Proficiency in programming languages such as Python, R, and Perl, and experience with scripting for automation.
• Strong knowledge of relevant biological databases (e.g., NCBI, Ensembl, UniProt) and their APIs.
• Experience with high-performance computing (HPC) environments and cloud computing platforms.
• Demonstrated ability to design and implement complex bioinformatics workflows.
• Excellent understanding of molecular biology, genetics, and genomics.
• Strong communication, presentation, and interpersonal skills, with the ability to effectively collaborate with diverse scientific teams.
• Experience in managing projects and mentoring junior researchers.
• Published research in high-impact scientific journals.

• Lead the development and application of bioinformatics pipelines for high-throughput sequencing data analysis.
• Design and execute complex computational experiments to answer biological questions.
• Develop and maintain robust, reproducible, and scalable analysis workflows.
• Collaborate closely with experimental biologists, geneticists, and clinicians to interpret results and generate hypotheses.
• Mentor and supervise junior bioinformaticians and research staff.
• Stay current with the latest advancements in bioinformatics, genomics, and related fields.
• Present research findings at international conferences and contribute to manuscript preparation.
• Manage data integrity, storage, and computational resources effectively in a remote environment.

• Ph.D. in Bioinformatics, Computational Biology, Computer Science, Statistics, or a related field.
• A minimum of 5 years of post-doctoral experience in bioinformatics or computational biology research.
• Demonstrated expertise in statistical analysis and machine learning methods applied to biological data.
• Proficiency in programming languages such as Python, R, and Perl, and experience with workflow management systems (e.g., Snakemake, Nextflow).
• Extensive experience with common bioinformatics tools, algorithms, and public databases (e.g., BLAST, GATK, UCSC Genome Browser, Ensembl).
• Experience analyzing various omics data types (NGS, single-cell, proteomics).
• Excellent communication and interpersonal skills, with the ability to work effectively in a remote, collaborative team setting.
• Proven ability to independently manage research projects and publish high-impact research.