Responsibilities
- Direct advanced descriptive, predictive, and inferential analyses using clinical, surveillance, and health systems data.
- Create and validate predictive models, risk stratification algorithms, forecasting models, and epidemiological or operational analytics.
- Apply rigorous statistical methods, hypothesis testing, and causal inference techniques to inform decision-making.
- Ensure all analytics are clinically interpretable, policy-relevant, and scientifically defensible.
- Design robust feature engineering pipelines using data from EMRs, HMIS, LMIS, and surveillance platforms.
- Support AI Engineers by providing high-quality training datasets, feature selection, bias detection, and data representativeness testing.
- Contribute to developing machine learning models, especially for tabular health data, time-series forecasting, and population-level risk prediction.
- Lead implementation of data quality frameworks, including checks for completeness, consistency, accuracy, and timeliness.
- Perform data audits, outlier detection, bias analysis, and missing data analysis.
- Ensure compliance with national health data governance standards, privacy policies, and ethical data use guidelines.
- Maintain comprehensive data documentation, metadata standards, and lineage tracking.
- Develop decision-support analytics and public health dashboards for tracking disease burden, resource allocation, and service delivery optimization.
- Translate analytical outputs into policy briefs, executive dashboards, and technical reports.
- Ensure outputs are actionable, explainable, and aligned with national health priorities.
- Collaborate closely with Data Engineers, integration teams, and AI Engineers to ensure analytics assets are scalable, reproducible, and production-ready.
- Support data pipeline optimization, ETL quality validation, and readiness for real-time and batch processing.
- Mentor junior data scientists and analysts.
- Lead internal analytics communities of practice, code reviews, and methodology workshops.
- Serve as a technical reviewer for analytics protocols, research designs, and data science deliverables.
- Stay current with advances in machine learning, statistical science, health analytics, artificial intelligence, and causal inference trends.
- Propose innovative analytics and data science use cases for public health, service delivery, health financing, supply chain and logistics for pharmaceuticals, and disease surveillance.
Other
- Language: English (implied by the posting being in English).
- Application deadline: October 12, 2026.
- Only shortlisted candidates will be contacted.
- In compliance with the data protection law of Rwanda, by submitting your application and CV, you explicitly consent to the collection, processing, and storage of your personal data by Clinton Health Access Initiative for the sole purpose of managing and conducting the recruitment process for the position for which you have applied.
