I'm a Data Science and Systems graduate passionate about building intelligent, data-driven solutions that bridge the gap between complex models and real-world impact. I specialise in machine learning, deep learning, statistical modeling, and production-aware AI engineering — from preparing high-quality datasets and training models to deploying them as APIs, dashboards, and scalable applications.
My academic research focused on handling extreme class imbalance and class overlap in binary classification on big data, giving me deep expertise in experiment design, model evaluation, and rigorous statistical analysis.
- 🔭 Currently working on end-to-end ML pipelines and AI-powered applications
- 🌱 Deepening my skills in MLOps, cloud AI workflows, and production monitoring
- 💡 Interested in applied AI, NLP, computer vision, and data engineering for AI systems
- 📫 Reach me at mutombatichaona@gmail.com
|
Building and evaluating predictive models with a focus on classification, model interpretability, and robust performance on challenging datasets — including imbalanced and overlapping class distributions. |
End-to-end analytical workflows: data extraction, cleaning, feature engineering, exploratory analysis, statistical testing, and interactive dashboarding for clear, decision-ready insights. |
|
Deploying models beyond notebooks — REST APIs with FastAPI, interactive apps with Streamlit, experiment tracking with MLflow, containerisation with Docker, and CI/CD-aware workflows. |
Designing reliable ETL pipelines, managing relational and NoSQL databases, ensuring data quality and validation, and building the data foundations that power AI systems. |
| Project | Description | Stack |
|---|---|---|
| Big Data Classification Research | Comparative analysis of techniques for extreme class imbalance and class overlap in binary classification | Python, Scikit-learn, Pandas, NumPy |
| Predictive Analytics Dashboard | End-to-end web app with ML model inference, API backend, and interactive frontend | Python, FastAPI, Streamlit, SQL, Plotly |
| Automated Data Pipeline | ETL pipelines for multi-source data extraction, transformation, and live dashboard reporting | Python, SQL, Power BI, Docker |
| ML Experiments & Notebooks | Collection of machine learning experiments, data analysis, and model evaluation workflows | Python, Jupyter, TensorFlow, PyTorch |
📂 Explore more on my repositories page
- ☁️ Cloud-based AI/ML workflows (AWS, GCP)
- 🔄 Advanced MLOps — model monitoring, automated retraining, and A/B testing
- 🧠 Advanced deep learning — transformers, attention mechanisms, and generative models
- 🏗️ Scalable data engineering for production AI systems
- ✍️ Technical writing and open-source contribution best practices
I'm always open to collaborating on AI/ML projects, discussing research ideas, or connecting with fellow data science enthusiasts.
Always learning. Always building. Always turning data into impact. ⚡