Ep. 365: Stefan Jansen on Agentic AI, ML Workflows, and the Evolution of Machine Learning for Trading

Stefan Jansen is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data & AI strategy, building data science teams, and developing end-to-end machine learning solutions. 

Before his current venture, he was a partner and managing director at an international investment firm, where he built the predictive analytics and investment research practice. He was also a senior executive at a global fintech company with operations in 15 markets, advised Central Banks in emerging markets, and consulted for the World Bank. In this podcast, we discuss: 

1. Defining AI as a Moving Target
2. The Trading vs. Business Data Science Divide
3. Synthetic Data and the “Fat Tail” Challenge
4. Shapley Values: Turning Black Boxes Grey
5. Agentic AI and Unstructured Data
6. RAG, Provenance, and the Context Window Debate
7. The “Alpha Factory” Workflow
8. Reinforcement Learning (RL) for Execution, Not Prediction
9. The Critical Role of Human Context
10. Advice for the AI-Native Job Market 

You can follow Stefan on LinkedIn.

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