GSMA AI Talent Mapping
Data Engineer & Analyst
About the Project
A consultancy research engagement for the GSMA African AI Language Models Initiative, Talent Workstream, mapping AI and LLM talent across 5 African regions and 7 skill clusters. The pipeline collects empirical signals from sources that each see a different slice of the market — job boards (Jobberman Nigeria and Ghana, Adzuna, Remotive), GitHub repositories and contributors, and academic publications via Google Scholar, Semantic Scholar and arXiv — then merges them into a single master analysis that feeds both an interactive dashboard and the written deliverables.
Key Highlights
- Built collectors across job boards, GitHub, and academic search, reconciling them into one master dataset via a repeatable rebuild step rather than manual merging
- Analysed AI talent across 5 African regions and 7 skill clusters, using GitHub topics, contributors and stars as practitioner signals alongside formal job demand
- Shipped the talent dashboard as a statically exported Next.js app, deployed both at a root domain and under a path prefix
- Produced the engagement's deliverables — a Talent Landscape Mapping and Pathways report, an executive brief, interview guides, and survey-response analysis
- Managed the Python toolchain with uv for reproducible collector runs across the project's lifetime
Technical Challenges
Aggregating talent signals from completely different sources into a unified view required careful normalization, because each source is biased in a knowable direction: GitHub over-represents open-source contributors and under-represents enterprise engineers, job APIs capture only formal advertised employment and miss the large informal market, and publication data skews toward academia over industry. Treating any one of them as ground truth would have produced a confidently wrong map, so the analysis weights them as complementary partial views and says so explicitly in the report.