Open to senior roles in survey methodology, small-area estimation, and population-health measurement, at statistics offices, health agencies, foundations, and research institutes. Full-time or contract.
Most interested where the work is producing trustworthy local estimates from fused survey, administrative, and geospatial data, and where rigorous methods and reproducible software both matter.
Methods & research
Building svy-sae, the small-area-estimation engine of the svy ecosystem (JAX): Fay–Herriot and unit-level models that fuse survey with administrative, census, and remote-sensing data, with proper MSE.
Writing the fusion-SAE methods-and-software paper as the flagship: scalable small-area estimation from multi-source data, shipped in production software.
Also svy-causal: survey-weighted IPTW and doubly robust estimation, validated against NHANES — being written up as a methods paper. A bounded methods probe, not a change of direction.
Platform
svyLab is at 327 passing tests and approaching public preview: the analytics platform built so that survey-correct estimation is reproducible and auditable by construction, with AI-assisted analysis that always shows its work.
Frontend in Astro + Svelte 5; backend in Python (Litestar) + PostgreSQL + DuckDB + Redis; AI layer via svy-agents.
Consulting
Selectively. Recently completed: NBS Tanzania capacity assessment for the World Bank / SADC; INSBU Burundi small-area estimation training (2 weeks); FeeLoST DHIS2-integrated platform for Ethiopia's Ministry of Health.
Open to short-term technical assistance on small-area estimation, survey design, data fusion, and reproducible analysis. English or French.
Reading & thinking about
Fusing survey, administrative, census, and geospatial data for reliable subnational estimates, and the uncertainty quantification that keeps them defensible.
Transportability and survey-weighted causal inference: extending design-based methods to populations a sample was not drawn from.
How AI-assisted analytics can get the statistics right by construction, rather than producing confident wrong numbers.
If something here resonates with what you're working on,
get in touch
.