Advanced proficiency in R (tidyverse, ggplot2, lme4, or equivalent packages); able to write modular, well-documented scripts and conduct independent code review.
Proficiency in SQL for querying, transforming, and managing structured datasets; able to write and optimise multi-table queries independently.
Familiarity with version control (Git or equivalent) and collaborative analytic workflows.
Deep knowledge of quantitative research methods and study design, including RCTs, pre-registered analyses, power calculations, and inferential statistics.
Ability to independently scope, plan, and deliver multi-month analytic workstreams with minimal supervision.
Rigorous attention to detail with a track record of catching and correcting errors before they reach downstream outputs.
Commitment to reproducibility as a non-negotiable standard, not a best-effort practice.
Ability to translate statistical findings into clear, decision-relevant language for non-specialist audiences without losing analytical integrity.
High professional standards in data ethics, confidentiality, and responsible use of participant data in a global health context.
Qualifications
Master's degree in statistics, data science, epidemiology, public health, economics, or a related quantitative field; or a Bachelor's degree with equivalent demonstrated research experience.
3 to 5 years of experience in data analysis, applied statistics, or quantitative research, with demonstrated ownership of analytic workstreams from start to publication or delivery.
Strong proficiency in R, including tidyverse, data.table, or equivalent; able to write and review production-quality analytic scripts without supervision.
Hands-on experience with RCT, longitudinal, or implementation evaluation datasets, including pre-registered analysis, randomization checks, and ITT/CACE estimation.
Proficiency in SQL for querying and managing structured databases.
Nice to Have
Experience with cloud-based or big data platforms (e.g., BigQuery, Spark) for work with large or complex datasets is a strong advantage.
Experience with Python for data analysis (e.g., pandas, NumPy), particularly where it extends into machine learning or advanced statistical modeling (e.g., classification, clustering) beyond standard regression-based methods, is a strong advantage.
Experience contributing to or co-authoring peer-reviewed publications or technical reports is a strong advantage.
GK
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