Overall Purpose of the Job

The Geospatial Data Officer will lead and coordinate data abstraction, cleaning, and integration processes to support high-quality data management across research projects within icipe’s 4-H paradigm (Environmental, Plant, Human, and Animal Health Themes). The Geospatial Data Officer will also contribute to the development and optimization of data infrastructure, ensuring accuracy, completeness, and timely delivery of datasets, while supporting capacity building and scientific outputs through training, documentation, and collaboration on research proposals and publications.

Specific duties

• Maintain and optimize data pipelines to support continuous updates and integration of spatial,
• epidemiological, and entomological data across multiple vector-borne disease (VBD) projects.
• Supervise and mentor junior staff and interns through structured onboarding, weekly checkins, and skill-building sessions
• Engage with external partners, data custodians, and researchers to acquire, harmonize, and curate relevant datasets, including unpublished or restricted-access data, for expanded project use.
• Design and deliver training workshops, webinars, and technical guidance to strengthen staff and partner capacity in geospatial data management, analysis, visualization, and application for research and decision-making.
• Collaborate with developers and GIS experts to co-develop automated data abstraction platforms that enhance the user interface and functionality of VBD platforms in icipe.

Requirements/ qualifications

• Bachelor’s in computer science, Bioinformatics, Population Biology, or related field, or any other related field from a recognized University.
• At least 4 years of work experience in a relevant area.
• Knowledge and skills in big data analytics.
• Good understanding of data security, data infrastructure, and governance.
• Knowledge of programming languages (e.g., R, C++, Python, and PERL).
• Experience in genomics, population genetics, data manipulation, and computational biology.
• Knowledge of commonly used tools for variant detection (samtools, bowtie2, BWA, IGV,
• ANNOVAR, and GATK) and file formats (BED, VCF, FASTQ, BAM, and FASTA).
• Knowledge of commonly used and publicly available bioinformatics databases (e.g. GenBank, Uniprot, DDBJ, Ensemble, GEO, SRA, ExAC, ESP,ClinVar, VectorBase, KEGG, HGMD,
• OMIM, PubMed, and UCSC
• Self-motivated with strong organizational and interpersonal skills and leadership skills