Senior Bioinformatician

About the role

M42 Health, a global AI‑driven health company headquartered in Abu Dhabi, is seeking a Senior Bioinformatician to join its multidisciplinary genomics team supporting the Emirati Genome Program. The role will lead complex bioinformatics analyses of large‑scale whole‑genome sequencing data and contribute to translational research, scientific discovery, and publications.

Key responsibilities

• Design and execute bioinformatics pipelines for whole‑genome sequencing datasets, including variant discovery and genotype‑phenotype analyses.
• Perform population genetics, association studies, rare‑variant and ancestry‑aware analyses, and family‑based investigations.
• Assess data quality, analytical assumptions, biases, and limitations to ensure scientific rigor and reproducibility.
• Integrate genomic, clinical, and phenotype data to support translational and clinically relevant research.
• Develop, optimise, and implement new bioinformatics workflows and evaluate emerging tools and technologies.
• Collaborate with scientists, clinicians, and technical teams to achieve cross‑functional research objectives.
• Contribute to scientific manuscripts, conference presentations, and other dissemination activities.

Required profile

• PhD in Bioinformatics, Human Genetics, Genomics, Computational Biology, Biostatistics, Computer Science or a related discipline (or equivalent advanced education and experience).
• Extensive experience leading complex genomic analysis projects with large‑scale whole‑genome sequencing data.
• Strong publication record in human genetics or genomics is preferred.
• Excellent analytical, problem‑solving, communication, and stakeholder‑management skills.
• Ability to work independently and as an effective member of multidisciplinary teams.

Required skills

• Proficiency in Python and/or R.
• Hands‑on experience with bioinformatics tools such as PLINK, REGENIE, bcftools, VCFtools.
• Deep understanding of human genetics, population genomics, variant analysis, and genotype‑phenotype relationships.
• Familiarity with clinical/phenotype data standards (e.g., OMOP, FHIR) is advantageous.
• Experience with machine learning, artificial intelligence, or large language model applications in genomics.