Salary: Per Ministry of Finance contract staff salary scale

Terms of employment: Contract

Duration of contract: One year with possibility of extension

Place of work: Ethiopian Public Health Institute, Addis Ababa

Quantity: Two

Background

The Ethiopian Public Health Institute (EPHI) is the national public health institute mandated to lead, conduct and coordinate the prevention and control of public health emergencies; strengthen the capacity of health laboratories and provide referral and reference testing services; and undertake research on priority public health and nutrition problems, programmes and strategies. EPHI is also mandated to establish and manage the national health data management and analytics center by gathering health and health-related data from multiple sources. The National Data Management and Analytics Center for Health (NDMC) manages national health data and applies advanced analytics, statistical modelling, machine learning, visualization and evidence synthesis to generate timely, policy-relevant evidence for decision-making at national and subnational levels.

With support from the Gates Foundation, EPHI is implementing the project “Strengthening Modeling Capacity to Inform Ethiopian Health Policies,” which supports the development and application of advanced analytical and modelling methods for priority public health questions. The project focuses on producing policy-relevant evidence, establishing reproducible analytical workflows, strengthening the skills of EPHI staff and improving collaboration with the Ministry of Health and public health programmes. The position will contribute to these objectives through its assigned technical responsibilities and capacity-strengthening activities.

Purpose of the Position

The purpose of the position is to lead and apply advanced data science to priority public health and policy questions, with particular emphasis on spatial machine learning, including deep learning, and spatial, temporal and spatiotemporal forecasting. The Senior Data Scientist will develop validated and reproducible analytical solutions from complex health, population, environmental and geospatial data; produce decision-relevant evidence; and strengthen EPHI capacity to use predictive analytics responsibly at national and subnational levels.

Roles and Responsibilities

3.1 Policy-linked Analysis and Data Preparation

1. Work with EPHI, Ministry of Health and programme teams to translate priority policy questions into clear analytical objectives, target outcomes, geographic units, prediction horizons, validation criteria and decision-relevant outputs.

2. Integrate and document survey, routine health information, surveillance, programme, population, climate, environmental, remote-sensing and other geospatial or time-series data; address data linkage, geocoding, temporal alignment, administrative-boundary changes and missingness.

3. Develop reproducible Python pipelines for spatial and temporal data preparation, feature engineering, quality assessment and model-ready datasets; use R where advantageous and Structured Query Language (SQL) for database work.

3.2 Advanced Machine Learning and Forecasting

4. Develop and compare spatial machine-learning models for risk mapping, hotspot detection, geographic prioritization, spatial prediction and small-area prediction, using spatially explicit validation that prevents leakage between nearby locations.

5. Apply deep-learning methods according to the data and purpose, including convolutional neural networks (CNNs) for imagery and gridded data, graph neural networks (GNNs) for connected or adjacency-based spatial data, and representation-learning or anomaly-detection methods for high-dimensional or unlabelled data.

6. Develop time-series and spatiotemporal forecasting models using strong baselines and suitable advanced methods such as Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), long short-term memory (LSTM) networks, gated recurrent units (GRUs), transformer-based models and the Temporal Fusion Transformer (TFT).

7. Quantify predictive uncertainty and evaluate calibration, discrimination, robustness, regional and subgroup performance, and practical usefulness through internal, temporal, spatial or external validation. Where appropriate, apply probabilistic forecasting, quantile regression or conformal prediction methods that account for temporal dependence and distribution shift.

8. Identify and address spatial and temporal data leakage, measurement error, selection bias, class imbalance, domain shift and other limitations that may affect model interpretation or generalizability.

3.3 Reproducible Products, Capacity and Governance

9. Develop version-controlled, tested and documented workflows in Python, with reproducible environments, data dictionaries, model cards and decision logs that can be maintained by EPHI staff.

10. Develop maps, dashboards, reusable libraries or application programming interfaces when these improve access to evidence, and coordinate with software and data-system teams for operational deployment.

11. Prepare technical reports, policy briefs, presentations and scientific manuscripts that clearly distinguish observed data, model assumptions, uncertainty, limitations and appropriate use.

12. Mentor analysts and modellers through hands-on spatial machine-learning, deep-learning and time-series projects, and deliver structured training on validation, uncertainty and responsible use of predictive models.

13. Collaborate with epidemiologists, geospatial analysts, statisticians, modellers, software developers, programme experts and policy stakeholders, and provide constructive technical review of analytical work.

14. Apply EPHI requirements for data access, confidentiality, security, ethical use, privacy, fairness and responsible automated prediction.

15. Contribute to work planning, progress reporting, partner coordination and other technically relevant assignments agreed with the supervisor.

Reporting

• Reports to: Project Principal Investigator (PI) / Director of the National Data Management and Analytics Center (NDMC) / Head of the Data Analytics, Modelling and Visualization Division.
• The position will be based at EPHI/NDMC in Addis Ababa.

Qualifications and Experience

• Doctor of Philosophy (PhD) in statistics, biostatistics, data science, computer science, artificial intelligence, public health informatics or a closely related quantitative field, with at least two years of relevant experience; or
• Master’s degree in one of the above fields, with at least six years of relevant experience.
• Demonstrated experience applying data science methods to public health, health systems, epidemiological or population data.

Desired Skills and Experience

• Advanced programming skills in Python are required. Proficiency in R is preferred, together with strong working knowledge of SQL.
• Demonstrated experience in spatial machine learning, geospatial data integration, remote-sensing data analysis, and time-series or spatiotemporal forecasting.
• Experience selecting, training and validating advanced models such as gradient-boosted trees, CNNs, GNNs, LSTM networks, GRUs and transformer-based forecasting models when appropriate to the data and decision need.
• Experience with spatial and temporal validation, predictive calibration, uncertainty quantification, model interpretability and responsible use of predictive models.
• Experience integrating and analysing large or heterogeneous health datasets and implementing robust data-quality procedures.
• Experience producing maps, visualizations and fit-for-purpose analytical applications using established Python, R and geospatial tools.
• Experience with Git-based collaboration, reproducible research, high-performance computing or secure cloud-computing environments is an advantage.
• Experience developing and delivering data science training and mentoring analysts.
• At least six public health-related articles published in reputable peer-reviewed journals, including at least three as first author.
• Strong analytical, problem-solving, scientific-writing and communication skills.
• Ability to work independently and collaboratively and to manage multiple tasks within agreed timelines.

Contract Duration and Remuneration

• The initial contract will be for one year, with possible extension based on satisfactory performance, project requirements and availability of funds.
• Salary will be determined in accordance with the applicable Ethiopian Public Health Institute and Ministry of Finance contract staff salary scale, taking into account the candidate’s qualifications and experience.