Point72
Quantitative Analyst, Structured Products Investment Team
New York, NY · Mid
Sponsorship not specifiedDetected 10 days ago
PythonC++ExcelResearch
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odds of building a lasting career here
42Risky
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
Lottery odds assume a STEM candidate.
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About the role
- Operating across 10 offices worldwide, we have doubled our capital allocation and grown our number of teams fivefold since 2020.
- We take care of our people We invest in our people, their careers, their health, and their well-being.
- For more information, visit https://point72.com/.
Responsibilities
- Help architect and maintain the infrastructure for Structured Products investing.
- Leverage internal and external partners to help with data infrastructure builds.
- Use Artificial Intelligence (AI) to enhance and optimize processes.
- Manage and assist in special projects within the CLO market.
- Support for employee-led affinity groups representing women, people of color and the LGBT+ community
- Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies.
Requirements
- Undergraduate degree or higher in computer science, economics, engineering, mathematics, physics, or statistics.
- Extensive experience with third-party structured credit cashflow engines (e.g., INTEX) and API.
- When you work here, we provide:
Benefits
- Fully-paid health care benefits
- Generous parental and family leave policies
- Tuition assistance
Company info
- We aim to attract and retain the industry's brightest talent by cultivating an investor-led culture and committing to our people's long-term growth.
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This listing is sourced directly from Point72's careers page and normalized into a canonical job model.