Available for Research & Collaboration

MOMAH MOSES .C.

Geospatial AI Engineer & Data Scientist

Building production-ready machine learning systems and geospatial intelligence platforms that address Africa's most critical development challenges — from flood early warning to epidemic surveillance.

10
Live GIS Dashboards
58+
Public Repositories
36
Nigerian States Covered
774
LGAs in WASH Analysis
// about me

Turning Data into
Development Impact

I am a Geospatial AI Engineer and Data Scientist based in Abuja, Nigeria, specialising in production-ready ML systems and spatial intelligence platforms that directly address Africa's development gaps.

My work covers the full data-to-decision pipeline — satellite imagery ingestion, PySpark ETL on Azure Databricks, spatial analysis with GeoPandas and Folium, through to deployed Streamlit dashboards used by planners and policymakers.

I am particularly focused on applications in public health, climate resilience, food security, and urban development — domains where better data infrastructure can save lives and direct billions in investment more effectively.

View All Projects
🛰️
Geospatial AI
GIS risk mapping, KDE hotspot analysis, satellite NDVI time series across 36 states
🏥
Healthcare AI
Federated learning, edge diagnostics, maternal mortality risk scoring with SHAP
⚙️
MLOps
FastAPI microservices, MLflow, drift monitoring, shadow deployment, auto-rollback
💳
FinTech & Risk
Credit scoring, fraud detection, causal inference for pricing optimisation
// tech stack

Tools & Technologies

End-to-end capability from raw data ingestion to deployed, monitored production systems.

Languages
Python
SQL
JavaScript
Bash
Machine Learning & AI
🔬 Scikit-learn
⚡ XGBoost
🔥 PyTorch
🎯 YOLOv8
💡 LightGBM
🔍 SHAP
🤗 HuggingFace
📊 MLflow
Geospatial
🗺️ GeoPandas
🌿 Folium
📐 Shapely
🛰️ Rasterio
🌍 GDAL
📡 Sentinel-2
Cloud, Big Data & MLOps
Azure
⚡ PySpark
🧱 Databricks
Docker
🚀 FastAPI
📈 Streamlit
📊 Plotly
🐼 Pandas

Projects & Systems

Production-grade platforms solving real problems across Nigeria and Africa.

🌊
Flood Risk Early Warning System
Climate & Disaster
🏥
Healthcare Access Gap Analyzer
Public Health
🌾
Agricultural Yield Predictor
Food Security
🚦
Traffic Congestion Intelligence
Urban Mobility · Lagos
Power Grid Outage Intelligence
Energy Infrastructure
💧
WASH Access Monitor — 774 LGAs
Water & Sanitation
🌳
Deforestation & Land Monitor
Environment
🦟
Disease Surveillance Tracker
Epidemiology
⚠️
Conflict & Security Analyzer
Peace & Security
🏙️
Urban Infrastructure Planner
Urban Development
🔬
Federated Cancer Detection — Africa
Privacy-preserving cancer detection across 6 African hospitals. Patient data never leaves the institution — trained via federated learning with differential privacy.
Federated Learning PyTorch Healthcare AI Africa
🦟
Malaria Parasite Detection
YOLOv8 blood smear analysis delivering 45-second malaria diagnosis at 97% accuracy — deployable on Raspberry Pi edge devices in resource-constrained clinics.
YOLOv8 Edge AI OpenCV Raspberry Pi
🏥
Maternal Mortality Risk Scoring
XGBoost + SHAP risk scoring for Nigerian PHCs with a DHIS2-compatible REST API and automated SMS alerts for community health workers.
XGBoost SHAP FastAPI Public Health
🛡️
Self-Healing Fraud Detection MLOps
Production fraud detection pipeline with FastAPI, MLflow experiment tracking, real-time drift monitoring, shadow deployment, and automatic model rollback.
MLflow FastAPI Docker Drift Detection
💳
SME Loan Default Prediction
ML ensemble predicting loan defaults for African microfinance banks, with SHAP explainability dashboards designed for non-technical loan officers.
XGBoost SHAP Credit Risk FinTech
🌿
Crop Disease Detection — Africa
Mobile-ready AI delivering 45-second crop disease diagnosis with treatment advice in Hausa, Yoruba, and Igbo — serving smallholder farmers across West Africa.
TensorFlow Mobile AI Hausa Yoruba
// get in touch

Let's Work Together

I am open to research collaborations, consulting engagements, and open-source contributions in geospatial AI, public health data science, and climate-resilient technology for Africa. Based in Abuja, available globally for remote work.

momahmoses@gmail.com