Bioinformatician

Job Title: Bioinformatician

Location: Edinburgh (Hybrid 2-3 Days On-site)

Salary: £40,000 – 60,000 D.O.E

Our client is seeking a talented bioinformatician to join their dynamic team in Edinburgh. This hybrid role requires the candidate to be based in, or willing to relocate to, Edinburgh.

Key Responsibilities:

• Develop and implement novel pipelines for single-cell and spatial transcriptomic data analysis.
• Assist in constructing and maintaining internal databases.
• Collaborate closely with the Bioinformatics team to advance our target discovery platform.
• Engage extensively with colleagues at our Cambridge R&D site.

Responsibilities Include:

• Running single-cell and single-nuclei RNA-seq analytical pipelines to identify therapeutic targets.
• Establishing spatial transcriptomic analysis pipelines.
• Keeping abreast of the latest methodologies in single-cell genomics.
• Working collaboratively with the scientific team and external partners.
• Effectively communicating and presenting data to various audiences.
• Maintaining accurate electronic records and code repositories.
• Contributing to the development of the drug discovery platform.

Experience and Requirements:

Essential:

• PhD or Masters in a relevant scientific discipline, or equivalent industry experience.
• Proficiency in single-cell transcriptome/genome data analysis.
• Experience in analysing and interpreting large datasets.
• Competence in statistical (e.g., R) and programming (e.g., Python, Java) languages.
• Strong analytical and statistical skills.
• Excellent communication skills in English.
• Good interpersonal and time management skills.
• Ability to work independently and troubleshoot new techniques.
• Ambition to contribute to a transformative company.

Desirable:

• Experience with 10x Genomics Chromium single-cell or single-nuclei transcriptomics data.
• Experience with 10x Genomics Xenium or VisiumHD spatial transcriptomics data.
• Experience in pipeline construction and implementation with Nextflow.
• Interest in machine learning applications in bioinformatics.