- sourcing and working with data sources,
- designing and evaluating ML models to inform trading decisions,
- developing, backtesting and executing trading strategies, and
- discovering new use cases and applications for ML in the trading domain.
The goal is to create a community of individuals with diverse backgrounds such as investment research and portfolio management, data science or software engineering interested in exchanging knowledge, ideas and experience around the dynamic topic of ML for trading.