Theoretical background: statistics, calculus and algebra.
ML experience: Computer Vision, Imbalanced classification, Deep Learning, Anomaly detection, Survival analysis, Recommenders.
ML stack: Jupyter, Pandas, Scikit-Learn, StatsModels, Tensorflow, Keras, Docker etc.
Coding skills: Python, JavaScript, Clojure, SQL, etc.