Principal Biostistician/Bioinformatics at Kenya Medical Research - KEMRI

Key Responsibilities:

• Participation in capacity building on bioinformatics by guiding MSc and PhD students on the project in both Kenya and Benin
• In charge of reception and storage of raw sequence data from the sequencing facilities, uploading to the high compute servers
• Take leadership in setting up virtual classroom discussions; small group discussions; data carpentry workshops, personalized virtual office hours with trainees on the statistical analyses of genomic data
• Participation in designing, review and update of the vector genomics and bioinformatics training curriculum
• Leadership in conceptualizing, innovating and development of new bioinformatic pipelines for analysis of genomic data
• Conduct bioinformatic analysis of raw sequence data to identify molecular markers associated with insecticide resistance in malaria vectors
• Attend conferences and workshops to disseminate research findings
• Leadership in drafting of technical reports, manuscripts, and outcomes of the project outputs
• Perform any other duties as assigned by the principal investigator

Vacancy Requirements:

• Master’s Degree in Bioinformatics; Statistics, Medical Statistics, Biostatistics, Health statistics, Biometry or equivalent qualification from a recognized Institution     
• Expertise in genomics, population genetics, data science, computational biology     
• Hands on experience with scripting languages such as Linux, Python, R and a strong grounding in biological sciences     
• Familiarity with commonly used tools for variant detection (samtools, bowtie2, BWA, IGV, ANNOVAR, GATK) and file formats (BED, VCF, FASTQ, BAM, FASTA)     
• Familiarity with commonly used and publicly available bioinformatics databases (e.g. GenBank, Uniprot, DDBJ, Ensemble, GEO, SRA, ExAC, ESP,ClinVar, VectorBase, KEGG, HGMD, OMIM, PubMed, UCSC)     
• Experience designing bioinformatics experiments and/or providing oversight for the design of 'omics/bioinformatics experiments.     
• Experience working with unstructured data sources, (experience with genome assembly)     
• An understanding of genome sequencing technologies and strategies.     
• Having a familiarity with Big Data analysis including data mining, complex data visualization.     
• Proficiency in computer application; and