Proven experience applying ML models to financial data sets (e.g., regression, classification, clustering, time series forecasting).
Strong ability to derive insights from structured and unstructured data. […]
Deep understanding of statistical methods and their application in financial modeling. […]
Strong understanding of Capital Markets, with a focus on Equities, Trading, and Market Microstructure. […]
Ability to interpret and model complex financial instruments and systems. […]
Advanced proficiency in Python for data science, including libraries such as NumPy, pandas, scikit-learn, TensorFlow...