Dynamic Asset Allocation across Crypto, Commodities, FX, Equities, and Prediction Markets
Ensemble Optimization: build quantitative models to determine optimal weights
Regime-Based Hedging: hedge tail risks with Prediction Markets and FX
Risk Budgeting: define VaR, stress tests, drawdown limits per sleeve
Cross-Asset Research: lead-lag relationships (DXY, yields) affecting Crypto and commodities
The Aggregator: optimize how trades fit together to improve IR

🎯 Requirements

5-10 years in Quant PM or Senior Allocation at a multi-strat hedge fund or family office
Multi-Asset Mastery: track record across at least three core asset classes
Prediction Markets experience (Polymarket, Kalshi) or Event-Driven Trading a plus
The Stack: Python (NumPy, Pandas, PyTorch, TensorFlow) and SQL/KDB+
Quantitative Depth: portfolio construction math, covariance estimation, L2 regularization
Education: Masters/PhD in Mathematics, Physics, CS, or Financial Engineering

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