Point72 Asset Management, L.P
Role:
Research analyst on a credit-focused discretionary macro portfolio management team. The analyst will work directly with the PM to conduct fundamental bottoms up research to generate commercial trade ideas across the full credit spectrum—Investment Grade and High Yield corporate bonds and their derivatives.
Responsibilities:
Build and maintain quantitative models for credit analysis, relative value identification, and risk management
Develop automated data pipelines for market data, fundamentals, and alternative datasets
Implement systematic signals and backtesting frameworks for trade idea generation
Create portfolio analytics, risk monitoring tools, and scenario analysis capabilities
Build relative value models across hundreds of securities in the credit universe
Automate repetitive analysis and reporting to improve team efficiency
Collaborate with the PM to translate investment hypotheses into testable quantitative frameworks
Monitor model performance, validate assumptions, and iterate on approaches
Requirements:
Bachelor's or Master's degree in computer science, statistics, mathematics, physics, engineering, or similar quantitative discipline
Strong programming skills in Python (pandas, numpy, scipy, scikit-learn) and SQL
Experience with data manipulation, statistical modeling, and time series analysis
Understanding of financial markets, particularly fixed income and credit derivatives
Ability to build production-quality code with proper testing and documentation
Strong problem solving skills and attention to detail in quantitative work
Good communication skills and ability to explain technical concepts to non-technical stakeholders
Intellectual curiosity and passion for applying quantitative methods to financial markets
Ability to take ownership of projects from conception to production
Hard working competitive spirit
Commitment to the highest ethical standards
The annual base salary for this role is $150,000 (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 based upon geographic location, work experience, education, and/or skill level, among other things.
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Responsibilities:
Build and maintain quantitative models for credit analysis, relative value identification, and risk management
Develop automated data pipelines for market data, fundamentals, and alternative datasets
Implement systematic signals and backtesting frameworks for trade idea generation
Create portfolio analytics, risk monitoring tools, and scenario analysis capabilities
Build relative value models across hundreds of securities in the credit universe
Automate repetitive analysis and reporting to improve team efficiency
Collaborate with the PM to translate investment hypotheses into testable quantitative frameworks
Monitor model performance, validate assumptions, and iterate on approaches
Requirements:
Bachelor's or Master's degree in computer science, statistics, mathematics, physics, engineering, or similar quantitative discipline
Strong programming skills in Python (pandas, numpy, scipy, scikit-learn) and SQL
Experience with data manipulation, statistical modeling, and time series analysis
Understanding of financial markets, particularly fixed income and credit derivatives
Ability to build production-quality code with proper testing and documentation
Strong problem solving skills and attention to detail in quantitative work
Good communication skills and ability to explain technical concepts to non-technical stakeholders
Intellectual curiosity and passion for applying quantitative methods to financial markets
Ability to take ownership of projects from conception to production
Hard working competitive spirit
Commitment to the highest ethical standards
The annual base salary for this role is $150,000 (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 based upon geographic location, work experience, education, and/or skill level, among other things.
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