| Skills: |
AI, Artificial Intelligence, Machine Learning, Portfolio Management, Portfolio Manager, Python, Quantitative Research, Risk Management, Stress Testing, Time Series, VaR |
| Description: |
Core to the health and growth of our business, our Information Technology organization powers the firm’s active, multi-manager model through flexible, scalable technology and advanced proprietary systems. Within this organization, the Central AI team focuses on modern AI/ML and data infrastructure that supports firm-level analytical capabilities and helps accelerate the next generation of decision-making tools across the platform.
Responsibilities:
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Design and build performance decomposition engines, including multi-factor residualization, exposure stability analysis, and regime-conditional analytics
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Develop similarity and uniqueness analytics, including strategy fingerprinting and factor and holdings overlap scoring
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Build marginal risk contribution analytics across VaR, expected shortfall, stress contribution, and crowding analysis
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Create portability and translation frameworks to support realistic analysis across platforms, constraints, and infrastructure environments
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Develop simulation, counterfactual, and strategy-specific analytical tooling across multiple investment styles
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Build skill and process decomposition models, along with evidence-tier weighting and confidence propagation across outputs
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Design capacity and scalability analytics covering liquidity, turnover, decay, and market impact
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Partner closely with investment and risk leadership to ensure methodologies are rigorous, durable, and credible under senior review
Required Skills
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Master’s degree with 7+ years of experience or PhD with 5+ years of experience in a quantitative field such as Statistics, Mathematics, Physics, Financial Engineering, Computer Science, Economics, or a related discipline
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Deep expertise in factor models, risk decomposition, performance attribution, portfolio overlap, stress testing, and scenario analysis, with a track record of building these frameworks directly
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Strong understanding of portfolio construction, risk analytics including VaR and expected shortfall, and portfolio optimization
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Advanced Python skills and experience working with large, complex financial datasets, including time series and panel data
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Strong statistical judgment, including experience with small-sample problems, selection bias, Bayesian methods, and uncertainty quantification
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Clear, effective communication skills, with the ability to translate quantitative findings for senior portfolio managers and non-technical stakeholders
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High intellectual honesty and sound judgment, including the confidence to challenge conclusions not supported by the data
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Experience in a multi-manager platform, hedge fund, fund-of-funds, prime broker, or risk analytics environment, with exposure to investment due diligence, strategy allocation, capacity modeling, or multi-strategy platform dynamics
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