Strong foundation in probability theory and statistical inference, including estimation, hypothesis testing, and distributional analysis
Experienced in building, tuning, and evaluating supervised learning models (classification and regression) with solid understanding of core ML algorithms
Skilled in applying unsupervised learning techniques such as clustering, dimensionality reduction, and association rule mining
Able to clean, preprocess, and engineer features from large, complex datasets for modeling and analysis
Proficient in data visualization using R (ggplot2) and Tableau, with emphasis on clarity and statistical storytelling
Strong interpretation skills: analyzing statistical outputs, diagnosing model behavior, and communicating insights clearly