Quantitative Researcher - Systematic Credit
at Point72
Location
Chicago, New York
Compensation
$150k–$200k USD
Type
full time
Posted
8/15/2024
Market range · function + seniority
p25 · target · p75 · n=779
Posted $200k · in the market band
Posting health
Aging · 65- ·Open 799 days
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Job description
Role
Quantitative Researcher for a new team focused on systematic corporate bond and credit derivatives strategies.
Responsibilities
- Independently conduct quantitative research, adopting a rigorous approach and using statistical and structural models
- Contribute to all aspects of the research and production process, including implementation of fitting tools; data organization; generation of alphas, risk and TC models; P&L attribution, etc.
- Proactively search for and prioritize new ideas and datasets for alpha potential
- Contribute to continuous improvement of the investment process and infrastructure in collaboration with the portfolio managers, developers and traders on the team
Requirements
- PhD or Master’s degree in Economics, Finance, Statistics, Mathematics, Physics, or other quantitative discipline
- 2+ years of experience developing statistical and fundamental alpha signals, risk factors for single name credit, equities, or options. Demonstrated ability to conduct research utilizing large data sets
- Experience with FICC, credit or option pricing models is preferred
- Experience with numerical optimization methods is a plus
- Solid programming skills: understanding of the object-oriented programming and CI/CD framework. Proficiency in Python, including with packages used for data research, best practices of coding style, etc.
- Strong communication skills
- Willingness to take ownership of his/her work, working both independently and within a team
The annual base salary range for this role is $150,000-$200,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
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