Bayesian Data Scientist

Job Title: Research Consultant - Modeling and Quantitative AnalysisJob Location: Fully Remote (EST OR PST)Job Duration: 6 Months (Possible extension)Hours: 20-35 Hours per weekJob Description:About the WorkDigital 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.Draws on indicators of connectivity, affordability, device access, and use.Is fitted as a multi-country, multi-year Bayesian panel model covering 27 countries.Uses an indicator set that is still expanding.Produces estimates at national and sub-national levels.Disaggregates results by gender and location.We are looking for an experienced modeler to work directly on the model itself, not only on the data feeding it.This is a temporary position:20–35 hours per week.Fully remote within the United States.Flexible hours week to week.Some overlap with colleagues across US Eastern and Pacific time zones is required for check-ins and review sessions.ResponsibilitiesWork on specification, estimation, and testing of the probabilistic segmentation model, including diagnostics on calibration and cross-country consistency.Investigate anomalies in model output and propose specification fixes, with the analysis to justify them.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.Required QualificationsCritical ThinkingAble to interrogate a modeling choice or a data source rather than accept it.Able to say plainly when something looks wrong. Strong CommunicationCan explain a method, a diagnostic result, or a caveat clearly, in writing and in conversation.Comfortable communicating with both technical and non-technical audiences. Strong Quantitative SkillsComfortable reasoning about probability, uncertainty, and distributions.Not limited to running estimation code. Comparable Modeling ExperiencePrior work specifying and estimating a model of similar structure.Experience deriving or validating a weighting scheme.Measurement or latent-trait index models, composite indicators, and population segmentation or classification models are considered close analogues. Bayesian AnalysisExperience with hierarchical and latent-variable specifications.Knowledge of prior specification, partial pooling, posterior diagnostics, and convergence diagnostics.Ability to judge how much structure a small number of units can support.Understanding that with 27 countries, priors and shrinkage carry substantial weight.Experience with panel methods built for large-N asymptotics transfers only partly. Unbalanced Panel DataExperience working with indicator coverage that differs by country and year.Understanding that data gaps are not random.Ability to determine when to:Impute,Marginalize,Let the measurement model absorb missingness.Also ImportantAttention to DetailRigorous about units, vintages, provenance, and documentation. Readiness to LearnWilling to learn the project's framework, indicators, and country context.Comfortable with a ramp-up period. PythonFluent in Python for data and modeling work.Experience with:pandasNumPySciPyComfortable using a version-controlled repository.Experience with probabilistic programming frameworks such as:PyMCNumPyroStan Building for ExtensionAble to accommodate new series and revised definitions without rebuilding specifications and code each time. Self-SufficiencyAble to pick up an existing codebase.Can make progress with limited direction.Raises blockers early.Nice to HaveResearch AbilityAble to find and synthesize relevant literature to inform methodological or measurement decisions. A Different AngleBrings a fresh perspective to problem-solving within an established framework. Additional ExperienceExperience with dynamic latent trait or cross-national measurement models used to build governance, democracy, or human rights indices.Familiarity with household survey programs such as:DHSLSMSGlobal FindexExperience with sub-national administrative data.Graduate training or professional background in:StatisticsEconomicsData ScienceDemographyRelated quantitative fieldsInterest in digital access and AI readiness in low- and middle-income country contexts.Mandatory SkillsDemonstrated experience specifying, estimating, and diagnosing Bayesian hierarchical or latent-variable models, evidenced by prior projects, publications, or code.Prior work building at least one comparable model end to end:Measurement or latent-trait index,Composite indicator,Population segmentation or classification model.Experience deriving or validating a weighting scheme.Experience working with multi-country or multi-year panel data with incomplete or uneven coverage.Python for statistical and data work, including:pandasNumPySciPyExperience with a probabilistic programming framework such as:PyMCNumPyroStanComfortable working in a version-controlled repository (Git).Able to pick up an existing codebase and work productively with limited supervision.Strong written communication skills.Able to clearly document methods, diagnostics, and caveats for both technical and non-technical audiences.Desired SkillsExperience with dynamic latent trait or cross-national measurement models, such as those used to build governance, democracy, or human rights indices.Familiarity with household survey programs such as:DHSLSMSGlobal FindexExperience with sub-national administrative or geospatial data.Experience structuring model code so new indicators and revised definitions can be added without a rebuild.Ability to find and synthesize methodological literature to inform a measurement decision.Graduate training or professional background in:StatisticsEconomicsData ScienceDemographyRelated quantitative fields.Interest in digital access and AI readiness in low- and middle-income country contexts.