Mounusha Metti

WORK HISTORY

EDUCATION

ABOUT MOUNUSHA METTI

I’m a Data Scientist and Machine Learning Engineer passionate about transforming data into intelligent, production-ready solutions that solve complex, real-world problems. My core strength lies in bridging the gap between data science experimentation and enterprise-scale AI deployment, using modern MLOps frameworks to make models reliable, explainable, and scalable.I bring a unique mix of machine learning expertise, cloud engineering, and domain-driven insight, having designed and deployed models that optimize infrastructure, forecast demand, and drive business efficiency. My experience spans across predictive modeling, geospatial analytics, sustainability intelligence, and customer analytics, delivering outcomes that blend technical rigor with real-world impact.My technical focus areas include- End-to-End Machine Learning Development: Skilled in data preprocessing, feature engineering, algorithm selection, and hyperparameter tuning using Python, Scikit-learn, TensorFlow, and PyTorch- MLOps & Model Deployment: Building CI/CD pipelines, automating retraining workflows, and deploying models via Docker and Kubernetes on Azure and AWS- Data Engineering & Infrastructure: Developing and maintaining robust data pipelines, integrating APIs, and managing version-controlled datasets for reproducible ML workflows- Performance Optimization: Implementing monitoring, A/B testing, and continuous model evaluation to ensure scalable and reliable production systems- Cross-Functional Collaboration: Working closely with engineers, analysts, and business leaders to translate strategic goals into actionable, data-driven solutions- Innovation & Research-Driven Development: Applying advanced optimization algorithms (e.g, genetic and multi-objective optimization) and explainable AI techniques to enhance decision-making.Across projects like Smart Water Infrastructure Optimization, Geospatial Housing Price Prediction, and Customer Churn Modeling, I’ve consistently delivered solutions that combine AI innovation with measurable operational value. My approach emphasizes clean, modular design, reproducibility, and long-term maintainability—hallmarks of modern AI engineering.Looking ahead, I’m focused on building intelligent, cloud-native systems that push the boundaries of automation and decision intelligence. I believe in continuous learning, responsible AI practices, and leveraging data-driven innovation to empower organizations toward smarter, more sustainable growth.