Artificial Intelligence and Machine Learning
End-to-end design and deployment of AI/ML systems for prediction, pattern recognition, anomaly detection, and decision automation. These systems may operate in real time and are designed for seamless integration into broader industrial, financial, or administrative processes. Implementation typically relies on Python-based frameworks, with cloud or containerized deployment used as needed.
Data Science and Statistical Analysis
Exploration and analysis of real-world datasets, including incomplete or noisy data. Development of models, operational metrics, and visualizations to support interpretation, monitoring, and data-informed decision-making. Statistical methods are selected contextually and implemented using tools such as R, Python, or domain-specific platforms.
