Quantitative Analyst - CIO Office
Point72 Asset Management - New York
Posted Aug 15, 2024
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Quantitative Analyst - CIO Office New York JOB DESCRIPTION Point72 Asset Management is seeking a Quantitative Analyst to join its Portfolio Construction & Analytics Team (PCAT) in the Office of the CIO. PCAT's mandate is to study all drivers of success for Long/Short Equities investment professionals using data, analytics, and models of investor behavior and the market. These analyses are shared with portfolio managers to improve their strategies and with senior management to better allocate capital across investment teams. The ideal candidate is a highly analytical and creative problem-solver who can conduct independent research, work effectively as part of a team, and effectively summarize and communicate findings. RESPONSIBILITES - Conduct bottoms-up analysis on the firm's portfolios to identify strengths and weaknesses in idea generation, trading, and construction - Form top-down views on which strategies offer the best risk/reward for the firm - Invent new analytics to quantify skill and frameworks to support trade-offs between different sources of skill and risk - Quantify market drivers to support pre-trade risk taking and decision making - Communicate key findings to the team, PMs, and Co-CIOs - Learn to develop in and contribute back to shared code base, reports, and research tools DESIRABLE CANDIDATES - 2 or more years of experience in a quantitative research, portfolio management, or risk management role dealing with equities or equities investments - Undergraduate, MS or Ph.D. in Finance, Computer Science, Mathematics, Engineering, or Physics, or other quantitative discipline - Experience with statistical models and essential methods of quantitative
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