[Hiring] Research Consultant @Metasys Technologies/NVISH

Role Description

Digital Planet is building a computational model and open-access interactive platform on AI readiness in low- and middle-income countries. The model classifies populations into segments according to what kind of AI-enabled service can realistically reach them, drawing on indicators of connectivity, affordability, device access, and use. It is fitted as a multi-country, multi-year Bayesian panel model covering 27 countries, with an indicator set that is still expanding. Estimates are produced at national and sub-national level and disaggregated by gender and location.

We are looking for an experienced modeler with fluency in Bayesian modeling to work directly on the model itself, not only on the data feeding it. This is a temporary position of 20–25 hours per week, fully remote within the United States. The team itself works across US Eastern and Pacific time, so either coast works well, but we do ask for some flexibility to overlap with colleagues in both zones for check-ins and review sessions.

Responsibilities

• Work on specification, estimation, and testing of the probabilistic segmentation model, including diagnostics on calibration and cross-country consistency.
• Investigate anomalies in model output, re-specify and re-run analyses in light of what the results show, and propose fixes with the analysis to justify them.
• Fit alternative specifications and complementary methods to the same data, document where their results converge and diverge, and combine them where useful so that different models bring out different features of the data.
• Separate primary from secondary drivers: condition on the dominant predictors of segment membership (e.g., device access, education) to surface secondary factors such as equity of access across gender, location, and income.
• Derive and test the weighting scheme that combines indicators into segment assignments, with weights estimated from data rather than assigned by judgment wherever possible.
• Build and maintain reproducible pipelines to clean and harmonize indicator data from a range of international and national sources.
• Reconcile indicators across differing vintages, definitions, and geographic levels; document assumptions and source hierarchies.
• Produce diagnostic tables, charts, and sensitivity analyses for internal and external review.
• Write clear code documentation and methodology notes suitable for public release.

Qualifications

• Bayesian analysis, specifically hierarchical and latent-variable specifications.
• Comparing models, not just fitting one.
• Critical thinking: Able to interrogate a modeling choice or a data source rather than accept it.
• Strong communication: Can explain a method, a diagnostic result, or a caveat clearly, in writing and in conversation, to both technical and non-technical readers.
• Strong quantitative and coding skills: Comfortable reasoning about probability, uncertainty, and distributions.
• A track record of building comparable models.
• Comfort with unbalanced panel data.

Requirements

• Probabilistic programming framework: Experience with a probabilistic programming framework such as PyMC, NumPyro, or Stan.
• Building for extension: Specifications and code should accommodate new series and revised definitions without a rebuild each time.
• Self-sufficiency: Can pick up an existing codebase, make progress with limited direction, and raise blockers early.
• Attention to detail: Rigorous about units, vintages, provenance, and documentation.
• Readiness to learn on the job: The framework, indicators, and country context are specific to this project.

Nice to have

• Research ability: Can find and synthesize relevant literature to inform a methodological or measurement decision.
• A different angle: Approaches problems differently from how the team does.
• Experience with dynamic latent trait or cross-national measurement models.
• Familiarity with household survey programs such as DHS, LSMS, or Global Findex.
• Graduate training or professional background in statistics, economics, data science, demography, or a related quantitative field.
• Interest in digital access and AI readiness in low- and middle-income country contexts.

Benefits