Clinical Bioinformatician, Cancer Centre

Bio Informatician, NGS

Department
Genomic Medicine

Entity
Cancer Centre

Location
Nairobi, Kenya

Introduction

Chartered in 1983, Aga Khan University (AKU) is a private, autonomous, and self-governing international university, with 13 teaching sites in 6 countries over three continents. An integral part of the Aga Khan Development Network, AKU provides higher education in multiple health science and social science disciplines, carries out research pertinent primarily to low- and middle-income countries and operates 7 hospitals (soon 8) and over 325 outreach clinics, all at international standards. It has almost 2,500 students and 14,000 staff. The University is both a model of academic excellence and an agent of social change. As a leading international institution dedicated to excellence and change, AKU operates on the core principles of quality, relevance, impact, and access.

Job Summary

The Clinical Bioinformatician will be responsible for analyzing and interpreting high-throughput genomic data to support accurate diagnosis and improved understanding of rare genetic disorders and cancer. The role involves processing and quality control of Next Generation Sequencing (NGS) data, identifying clinically relevant variants, and collaborating closely with laboratory scientists, clinicians, and genetic counselors to deliver high-quality, actionable genomic insights that inform patient care and research.

Responsibilities

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.

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

Personal Characteristics & Behaviours

• Analytical and Detail-Oriented: Demonstrates strong attention to accuracy and precision when handling complex genomic data and variant interpretations.

• Collaborative: Works effectively within interdisciplinary teams of clinicians, laboratory scientists, and researchers, fostering open communication and shared goals.

• Maintains the highest standards of integrity and confidentiality when handling sensitive patient genetic information.

• Organized and Accountable: Manages multiple analyses and reporting timelines efficiently while maintaining thorough documentation.

• Effective Communicator: Able to clearly explain complex genomic concepts to non-specialist audiences, including clinicians and administrators.

• Committed to continuous improvement and adherence to clinical laboratory standards and best practices