Requirements

• Master’s degree in bioinformatics, Computational Biology, Genomics, Molecular Biology, or a related field.
  
• A PhD in a relevant discipline is an added advantage.
  
• Additional training or certification in clinical genomics, molecular diagnostics, or data science is desirable.
  

Relevant Experience

• Minimum 2–3 years of experience in clinical or translational bioinformatics, ideally with NGS data analysis.
  
• Proficiency with bioinformatics tools and programming languages (e.g., Python, R, bash, workflow managers like Nextflow or Snakemake).
  
• Experience with standard variant annotation and filtering tools (e.g., VEP, ANNOVAR, GATK, bcftools, samtools).
  
• Strong working knowledge of human genetics, including Mendelian inheritance, variant pathogenicity, and cancer genomics.
  
• Familiarity with clinical variant classification guidelines (e.g., ACMG, AMP, CAP).
  
• Excellent analytical and problem-solving skills.
  
• Effective verbal and written communication skills to present results to non-technical audiences
  

Genomic Data Analysis and Interpretation

• Process and analyze NGS data from germline and somatic sources (e.g., whole exome sequencing, targeted panels).
  
• Perform quality control, alignment, variant calling, annotation, and filtering of sequencing data.
  
• Interpret and classify genetic variants based on clinical relevance using established guidelines (e.g., ACMG/AMP).
  

Clinical Reporting and Collaboration

• Collaborate with clinical laboratory scientists, physicians, and genetic counselors to review variant interpretations.
  
• Contribute to multidisciplinary discussions for patient case reviews.
  
• Participate with clinicians in preparing clinical reports that summarize genomic findings in a clear and actionable manner.
  

Data Management, Tools, and Pipeline Optimization

• Maintain and enhance bioinformatics pipelines and databases used in variant analysis.
  
• Implement best practices for data management, version control, and documentation.
  
• Evaluate new bioinformatics tools and methods to improve analysis accuracy and efficiency.
  

Research, Innovation, and Capacity Building

• Support research projects focused on rare genetic disorders and cancer genomics.
  
• Contribute to publications, presentations, and development of new analytical approaches.
  
• Provide training or mentorship to junior staff and students in genomic data analysis.