Core Responsibilities
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Research and implement multi-factor models integrating fundamental, technical, and unstructured data
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Develop ML-driven stock ranking systems using alternative datasets (satellite imagery, supply chain data, earnings call NLP)
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Optimize portfolio construction with constrained risk models (Barra/APT frameworks)
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Conduct regime-switching analysis to adapt strategies across market cycles
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Collaborate with data engineers to build proprietary datasets from SEC filings/transcripts
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Implement transaction cost models incorporating global equity market microstructure
Essential Qualifications
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Advanced degree (PhD/MS) in Financial Engineering, Computational Finance, or Applied Mathematics
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2+ years experience in:
✅ Equity factor development (value, quality, momentum, crowding)
✅ Portfolio optimization techniques (non-convex optimization, Bayesian shrinkage)
✅ Python quant stack (pandas, PyTorch, QuantLib, Alphalens) -
Deep understanding of:
▶️ Equity derivative pricing and hedging
▶️ Corporate actions processing and adjustment methodologies
▶️ Global equity market structure (NYSE/NASDAQ/LSE/HKEX) -
Track record of developing production-ready alpha signals (IC > 5%)