Available for collaboration
Hi, I’m Leykun Getaneh
Data Scientist · Statistician · Data Analyst
I bring 10+ years of experience in research, analytics, and data science, applying R, Python, and modern machine learning and deep learning methods to solve challenges in disease forecasting, predictive modeling, and public health decision-making. My work focuses on turning complex data into actionable insights and decision-support tools.
10+ Years of experience
6+ Data products deployed
5+ Languages & tools
2 Peer-reviewed publications
What I do
From raw data to real-world decisions
I combine statistical rigor with engineering to deliver analytics that public health teams can act on.
Modeling & Forecasting
Statistical, machine learning, and deep learning models for disease prediction and time-series forecasting — including transformer-based approaches like Temporal Fusion Transformers.
Public Health Analytics
Analytics pipelines and decision-support systems for surveillance, early warning, and demographic health data — from woreda level to national scale.
Interactive Data Products
Dashboards, prediction apps, and automated reports that make results accessible and reproducible for non-technical stakeholders.
Selected work
Featured projects
A few products spanning disease forecasting, public health analytics, HDSS, and dashboards.
Disease Forecasting
Malaria Forecasting Dashboard
Interactive forecasting system for public health monitoring — real-time visualization of forecasted incidence supporting woreda, regional, and national decisions. Built with R / Python Shiny.
Public Health · HDSS
impactHDSS Analytics Platform
Analytics & Visualization lead for a multi-site Health & Demographic Surveillance System — building pipelines and dashboards that turn longitudinal population data into insight.
Machine Learning
Childhood Micronutrient Deficiency
ML prediction tool estimating childhood micronutrient deficiency from demographic and health features — an XGBoost model built with the Tidymodels framework.
Dashboard
COVID-19 Dashboard: Ethiopia
A comprehensive view of the COVID-19 epidemic in Ethiopia — epidemiological trends, case statistics, and visualizations for evidence-based response. Built with R and Quarto.
Background
Experience & education
Experience
Data Scientist National Data Management & Analytics Center (NDMC), EPHI Jan 2025 – Present
Lecturer · Data Scientist Wollo University Sep 2016 – Present
Education
MSc in Statistics Addis Ababa University, Ethiopia Sep 2010 – Jan 2013
BSc in Statistics University of Gondar, Ethiopia Sep 2006 – Jul 2008