AI / Data Science Officer

AAU - College of Health Science - AI Project Addis Ababa · Addis Ababa Posted 10h ago

About this role

ADDIS ABABA UNIVERSITY COLLEGE OF HEALTH SCIENCES DEPARTMENT OF PEDIATRICS AND CHILD HEALTH AI-Based Childhood Pneumonia, Malaria, and Diarrhea Case Surge Prediction System in Ethiopia Project

EXTERNAL VACANCY ANNOUNCEMENT

Position: AI / Data Science Officer

Duty Station: Addis Ababa, Ethiopia

Number of Positions: One (1)

Employment Type: Full-time, Contract

Contract Duration: 6 Month contract with possibility Extension.

Salary: Negotiable

Role summary: Leads the technical, hands-on development and validation of the forecasting models, working closely with our partners and technical team and reporting to the PI.

Responsibilities:

  • Build and maintain the data pipelines that ingest and harmonize DHIS2, EPHI weekly surveillance, meteorological (ERA5/CHIRPS/EMI), and 952 call-center data into an analytical environment.
  • Develop, train, and validate the forecasting and anomaly-detection models (e.g., SARIMAX, Prophet, Random Forest, XGBoost, LSTM) using the open-source Python stack, applying walk-forward temporal validation.
  • Implement data cleaning, feature engineering, and missing-data handling per the protocol, in a version-controlled and reproducible manner.
  • Work in close coordination with partner organizations on data access, cleaning, integration, modeling, and testing.
  • Support development of dashboards and alert outputs aligned with Ministry of Health workflows.
  • Document all code, pipelines, and model versions, and support the transition/handover of the open-source system to Ministry-managed infrastructure.
  • Present technical progress during the weekly virtual meetings and report to the Principal Investigator.

Reporting line: Principal Investigator, with close technical coordination with partner organizations.

Requirements

Qualifications: Degree in data science, computer science, biostatistics, or related field; demonstrated experience in machine learning / time-series modeling in Python; experience with health or surveillance data an advantage.

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