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1. As a minimum – GRADUATE DEGREE in one of the following: Financial Engineering, Applied Mathematics, Computational Finance, Machine Learning, Statistics, Probability Theory, Advanced Mathematics
2. PhD strongly preferred, but not required
AREAS OF EXPERTISE:
1. Financial Engineering, “Quant” analyst of investments, Computational Finance, Applied Mathematics, Portfolio Theory, Chaos and Complexity Theory, Machine Learning, Predictive Modeling, Risk Scoring
2. CRITICAL AREA (1): Ability to apply MACHINE LEARNING TECHNIQUES for investment analysis of nonlinear behavior (at least a subset of the below techniques). This includes (and not limited to):
- Neural Networks
- Decision Trees
- Kalman Filtering
- Hotspot Analysis
- Bayesian Networks
- Reinforcement Learning
- Support Vector Machines
- Association Rule Learning
- Inductive Logic Programming
- Genetic Programming
- Representation Learning
3. CRITICAL AREA (2): Demonstrated using machi...
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