Self-taught ML Engineer from Kisumu, Kenya 🇰🇪 — building production-ready ML systems that solve real African problems, from flood prediction to salary transparency.
With a strong foundation in Physics and Mathematics from Moi University and hands-on geophysical research at KenGen's Olkaria Geothermal Project, I bring scientific rigour to every ML system I build — coded entirely on Android (Termux + PyramIDE).
- 🎓 B.Sc. Physics (Major) + Mathematics (Minor) — Moi University, 2012
- 🏭 Industrial Attachment — KenGen Olkaria Geothermal Project, 2011 (MT and TEM methods)
- 🔬 Research — Eburru Geothermal Prospect geophysical study — Prof. Mghendi Mwamburi
- 📍 Location — Kisumu, Kenya (near Lake Victoria)
- 🕒 Timezone — EAT (UTC+3)
- 🌐 Open To — Remote ML Engineer / Data Science roles — US · EU · Global
- 🧠 Build end-to-end ML systems — raw data to feature engineering to model training to live API
- 📐 Apply Physics and Mathematics background to feature engineering and model evaluation
- 🌍 Solve real African problems — flood risk, salary transparency, credit scoring
- 🚢 Ship production-grade code — FastAPI · Docker · GitHub Actions CI/CD
- 📊 Explain every prediction with SHAP — because unexplainable AI is not good enough
Languages and Data
Machine Learning and AI
Visualisation
Web and Deployment
| Project | Description | Stack | Live |
|---|---|---|---|
| 🌊 Nyando Flood AI | GradientBoosting on 2,308 GEE satellite points — AUC 0.97 — 50K residents | sklearn · FastAPI · Docker · GEE | API |
| 🚢 Titanic Survival | Leak-free LR Pipeline · SHAP waterfalls · Bootstrap CIs · 13 charts | sklearn · FastAPI · Streamlit | Demo |
| 💼 AfriSalaries | XGBoost salary band classifier — 8 African countries · E2E 88% accuracy | XGBoost · FastAPI · Vercel | App |
| 🏦 Loan Risk | Basel III framing · Gini 0.74 · IFRS 9 staging · EL = PD × LGD × EAD | sklearn · pandas | — |
| Status | Project | Domain | Data |
|---|---|---|---|
| ✅ Live | Nyando Flood Risk AI | Climate / Disaster | GEE Satellite |
| ✅ Live | Titanic Survival Prediction | Education / Portfolio | Kaggle |
| ✅ Live | AfriSalaries Classifier | Labour Economics | Web-scraped |
| ✅ Live | Loan Risk Assessment | FinTech / Banking | Synthetic + Real |
| 🔄 In Progress | CMDMS Church Management | SaaS / Web App | PostgreSQL |
| 🔜 Planned | Crop Disease Detection | Computer Vision | PlantVillage |
| 🔜 Planned | Malaria Outbreak Prediction | Public Health | WHO, DHIS2 |
| 🔜 Planned | Flood Risk Mapping v2 | Geospatial ML | CHIRPS |
| 🔜 Planned | M-Pesa Fraud Detection | FinTech | Transactional |
| 🔜 Planned | Credit Scoring Unbanked | Finance | Alternative |
| 🔜 Planned | Solar Potential Mapping | Energy | NASA POWER |
| 🔜 Planned | Matatu Route Optimisation | Transport | OSM, GTFS |
| 🔜 Planned | Lake Victoria Water Quality | Environment | Satellite + IoT |
| Certificate | Issuer | Date |
|---|---|---|
| Machine Learning using Python | Programming Hub / Google Developers Launchpad | Oct 2025 |
| Python Basics | Programiz | Sep 2025 |
B.Sc. Physics (Major) + Mathematics (Minor) — Moi University, Kenya | 2008–2012
- Classical Mechanics · Statistical Physics · Linear Algebra · Calculus · Numerical Methods
- Research: Eburru Geothermal Prospect using MT and TEM methods — Prof. Mghendi Mwamburi
Industrial Attachment — KenGen Olkaria Geothermal Project | 2011
- Large-scale geophysical survey data collection and processing in the field
- Applied MT and TEM subsurface imaging — first exposure to scientific data pipelines
"I build production ML systems — not just notebooks. My Physics and Mathematics background means I think carefully about what a model is actually measuring before I trust its output. Every project I ship has a live URL, SHAP explainability, and a real problem it solves — because that is what actually helps people and what gets you hired."
- Prof. Johan Loeckx — VUB AI Lab, Vrije Universiteit Brussel, Belgium
- Prof. Samuel Liyala — JOOUST, Kenya
Available for remote ML / Data Science roles — US · EU · Global
Building from Kisumu, Kenya — one model at a time 🌍