Key Responsibilities:
Lead the design and execution of all primary quantitative analyses, impact evaluation, and effective coverage estimation.
Specify detailed statistical analysis plans.
Develop, maintain, and govern harmonised, reproducible analytic codebases (R, Stata, Python), ensuring compatibility with federated data execution and secure in-country runs.
Oversee data preparation for all analyses, including variable construction, temporal alignment, cohort definition, and linkage across routine and evaluation datasets.
Implement and supervise advanced data quality assurance and reproducibility processes.
Lead the Year 1 mortality recalibration analysis, comparing modelled projections with observed empirical data and updating lives-saved estimates.
Co-lead statistical quality control processes across countries, ensuring consistency of model implementation, outputs, and reporting.
Mentor quantitative analysts and work closely with the Senior Data Management RO to align analytic needs with data pipelines and governance requirements.
Produce high-quality statistical outputs, including technical appendices, codebooks, metadata, and fully reproducible analytic notebooks to support reporting, peer-reviewed publications, and external review.
Vacancy Requirements:
PhD in Biostatistics/Statistics/Data Science (or equivalent) Mandatory
At least 5 years post-doctoral research experience. Mandatory
Demonstrated expertise in ITS, causal inference in observational settings, multilevel models, and routine health data. Mandatory
Advanced R/Stata/Python; reproducible research; version control; clear technical writing Mandatory