أخصائي معلوماتية حيوية أول

Overview

M42 is a global health champion powered by artificial intelligence (AI), technology and genomics to advance innovation in health for people and the planet. Headquartered in Abu Dhabi, M42 combines its specialized, state-of-the-art facilities with integrated health solutions like genomics and biobanks, and harnesses advanced technologies to deliver precise, preventive and predictive care, to disrupt traditional healthcare models and positively impact lives globally.

The Senior Bioinformatician will join a multidisciplinary genomics and bioinformatics team supporting the Emirati Genome Program (EGP), a national-scale whole genome sequencing initiative. The role is responsible for independently leading complex bioinformatics and human genetics analyses, applying robust analytical methodologies, and introducing innovative tools and approaches that enhance the organization's scientific and technical capabilities. The position contributes to translational research, scientific discovery, and publication activities through the analysis of large-scale genomic and phenotype-linked datasets.

Responsibilities

• Lead the design and execution of bioinformatics analyses on large-scale human genomic datasets, including whole genome sequencing (WGS) data.
• Perform genomic analyses including variant discovery, genotype-phenotype analysis, population genetics, association studies, rare variant analysis, ancestry-aware analysis, and family-based genomic investigations.
• Analyze and interpret genomic findings within the context of human genetics, population structure, clinical phenotypes, and scientific evidence.
• 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, optimize, and implement bioinformatics workflows, pipelines, and analytical methodologies.
• Evaluate emerging technologies, tools, datasets, and analytical approaches and introduce appropriate innovations to enhance team capabilities.
• Collaborate with bioinformaticians, scientists, clinicians, researchers, and technical teams to deliver cross-functional research objectives.
• Contribute to scientific publications, manuscripts, technical reviews, presentations, and conference activities.
• Communicate analytical methods, results, risks, assumptions, and recommendations effectively to technical and non-technical stakeholders.
• Ensure compliance with organizational policies, regulatory requirements, data governance standards, and scientific best practices.
• Support continuous improvement initiatives that enhance analytical quality, operational efficiency, and scientific outcomes.

Qualifications

• PhD in Bioinformatics, Human Genetics, Genomics, Computational Biology, Biostatistics, Computer Science, or a related discipline.
• Equivalent combination of advanced education and relevant experience may be considered.
• Extensive experience in human genomics, bioinformatics, computational biology, or biomedical data science.
• Proven experience independently leading complex genomic analysis projects.
• Experience working with large-scale genomic datasets and whole genome sequencing data.
• Strong scientific publication record preferred.
• Strong proficiency in Python and/or R.
• Hands-on experience with bioinformatics tools such as PLINK, REGENIE, bcftools, VCFtools, or equivalent platforms.
• Strong understanding of human genetics, population genomics, variant analysis, and genotype-phenotype relationships.
• Experience with clinical, phenotype, or longitudinal healthcare datasets is advantageous.
• Knowledge of OMOP, FHIR, EHR-derived phenotypes, or healthcare data standards is desirable.
• Experience with machine learning, artificial intelligence, or large language model applications in genomics is an advantage.
• Excellent analytical, problem-solving, communication, and stakeholder management skills.
• Demonstrated ability to work independently while contributing effectively within multidisciplinary teams.

• Lead the design and execution of bioinformatics analyses on large-scale human genomic datasets, including whole genome sequencing (WGS) data.
• Perform genomic analyses including variant discovery, genotype-phenotype analysis, population genetics, association studies, rare variant analysis, ancestry-aware analysis, and family-based genomic investigations.
• Analyze and interpret genomic findings within the context of human genetics, population structure, clinical phenotypes, and scientific evidence.
• 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, optimize, and implement bioinformatics workflows, pipelines, and analytical methodologies.
• Evaluate emerging technologies, tools, datasets, and analytical approaches and introduce appropriate innovations to enhance team capabilities.
• Collaborate with bioinformaticians, scientists, clinicians, researchers, and technical teams to deliver cross-functional research objectives.
• Contribute to scientific publications, manuscripts, technical reviews, presentations, and conference activities.
• Communicate analytical methods, results, risks, assumptions, and recommendations effectively to technical and non-technical stakeholders.
• Ensure compliance with organizational policies, regulatory requirements, data governance standards, and scientific best practices.
• Support continuous improvement initiatives that enhance analytical quality, operational efficiency, and scientific outcomes.

• PhD in Bioinformatics, Human Genetics, Genomics, Computational Biology, Biostatistics, Computer Science, or a related discipline.
• Equivalent combination of advanced education and relevant experience may be considered.
• Extensive experience in human genomics, bioinformatics, computational biology, or biomedical data science.
• Proven experience independently leading complex genomic analysis projects.
• Experience working with large-scale genomic datasets and whole genome sequencing data.
• Strong scientific publication record preferred.
• Strong proficiency in Python and/or R.
• Hands-on experience with bioinformatics tools such as PLINK, REGENIE, bcftools, VCFtools, or equivalent platforms.
• Strong understanding of human genetics, population genomics, variant analysis, and genotype-phenotype relationships.
• Experience with clinical, phenotype, or longitudinal healthcare datasets is advantageous.
• Knowledge of OMOP, FHIR, EHR-derived phenotypes, or healthcare data standards is desirable.
• Experience with machine learning, artificial intelligence, or large language model applications in genomics is an advantage.
• Excellent analytical, problem-solving, communication, and stakeholder management skills.
• Demonstrated ability to work independently while contributing effectively within multidisciplinary teams.