Quantitative Strategist
Mondrian Alpha
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New York, NY
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Associate
Posted April 2, 2026
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Responsibilities
Responsibilities
- Develop and implement machine learning / AI-driven models for alpha generation, pricing, and risk analysis
- Apply advanced statistical and quantitative techniques to fixed income and structured credit instruments (e.g., ABS, MBS, CLOs, corporate credit)
- Build scalable research pipelines for feature engineering, model training, and validation
- Conduct exploratory data analysis on large, complex financial and alternative datasets
- Collaborate with portfolio managers to translate research insights into production-ready strategies
- Stay at the forefront of advances in AI/ML (e.g., deep learning, LLMs, reinforcement learning) and apply them to financial markets Required Qualifications
Not Met Priorities
What still needs stronger evidence
Requirements
- Conduct exploratory data analysis on large, complex financial and alternative datasets
- Stay at the forefront of advances in AI/ML (e.g., deep learning, LLMs, reinforcement learning) and apply them to financial markets Required Qualifications
- Strong programming skills in Python (experience with C++ is a plus)
- Deep understanding of machine learning techniques, including modern deep learning frameworks (e.g., PyTorch, TensorFlow)
- Solid grounding in probability, statistics, and optimization
- Experience working with large datasets and distributed computing environments Preferred Experience
- Prior experience in structured credit, fixed income, or credit derivatives markets
- Familiarity with financial instruments, pricing models, and risk frameworks in credit markets
- Experience applying AI/ML techniques to time series, market microstructure, or asset pricing problems
- Opportunity to work on high-impact, real-world AI applications in finance
- Collaborative environment combining top-tier investors, quants, and technologists
Preferred Skills
- Stay at the forefront of advances in AI/ML (e.g., deep learning, LLMs, reinforcement learning) and apply them to financial markets Required Qualifications
- Strong programming skills in Python (experience with C++ is a plus)
- Experience working with large datasets and distributed computing environments Preferred Experience
- Strong preference for candidates with experience in leading AI labs or research environments (e.g., industry AI labs, top-tier academic labs, or equivalent)
- Prior experience in structured credit, fixed income, or credit derivatives markets
- Experience applying AI/ML techniques to time series, market microstructure, or asset pricing problems
- Track record of published research, open-source contributions, or production ML systems What We Offer
- Opportunity to work on high-impact, real-world AI applications in finance
- Collaborative environment combining top-tier investors, quants, and technologists
- Access to unique datasets and significant computational resources
Education
- (Not required) – Advanced degree (PhD preferred) in Computer Science, Machine Learning, Applied Mathematics, Physics, Engineering, or related field
We are a technology-driven hedge fund seeking a Quantitative Analyst or Quantitative Strategist with a strong background in artificial intelligence and machine learning to join our investment team. This role sits at the intersection of systematic investing, data science, and applied research, with a particular emphasis on structured credit and fixed income markets. You will work closely with portfolio managers, researchers, and engineers to design and deploy cutting-edge models that generate alpha and enhance risk management across credit-focused strategies. Key Responsibilities
Develop and implement machine learning / AI-driven models for alpha generation, pricing, and risk analysis
Apply advanced statistical and quantitative techniques to fixed income and structured credit instruments (e.g., ABS, MBS, CLOs, corporate credit)
Build scalable research pipelines for feature engineering, model training, and validation
Conduct exploratory data analysis on large, complex financial and alternative datasets
Collaborate with portfolio managers to translate research insights into production-ready strategies
Stay at the forefront of advances in AI/ML (e.g., deep learning, LLMs, reinforcement learning) and apply them to financial markets Required Qualifications
Advanced degree (PhD preferred) in Computer Science, Machine Learning, Applied Mathematics, Physics, Engineering, or related field
Strong programming skills in Python (experience with C++ is a plus)
Deep understanding of machine learning techniques, including modern deep learning frameworks (e.g., PyTorch, TensorFlow)
Solid grounding in probability, statistics, and optimization
Experience working with large datasets and distributed computing environments Preferred Experience
Strong preference for candidates with experience in leading AI labs or research environments (e.g., industry AI labs, top-tier academic labs, or equivalent)
Prior experience in structured credit, fixed income, or credit derivatives markets
Familiarity with financial instruments, pricing models, and risk frameworks in credit markets
Experience applying AI/ML techniques to time series, market microstructure, or asset pricing problems
Track record of published research, open-source contributions, or production ML systems What We Offer
Opportunity to work on high-impact, real-world AI applications in finance
Collaborative environment combining top-tier investors, quants, and technologists
Competitive compensation with strong performance incentives
Access to unique datasets and significant computational resources
Develop and implement machine learning / AI-driven models for alpha generation, pricing, and risk analysis
Apply advanced statistical and quantitative techniques to fixed income and structured credit instruments (e.g., ABS, MBS, CLOs, corporate credit)
Build scalable research pipelines for feature engineering, model training, and validation
Conduct exploratory data analysis on large, complex financial and alternative datasets
Collaborate with portfolio managers to translate research insights into production-ready strategies
Stay at the forefront of advances in AI/ML (e.g., deep learning, LLMs, reinforcement learning) and apply them to financial markets Required Qualifications
Advanced degree (PhD preferred) in Computer Science, Machine Learning, Applied Mathematics, Physics, Engineering, or related field
Strong programming skills in Python (experience with C++ is a plus)
Deep understanding of machine learning techniques, including modern deep learning frameworks (e.g., PyTorch, TensorFlow)
Solid grounding in probability, statistics, and optimization
Experience working with large datasets and distributed computing environments Preferred Experience
Strong preference for candidates with experience in leading AI labs or research environments (e.g., industry AI labs, top-tier academic labs, or equivalent)
Prior experience in structured credit, fixed income, or credit derivatives markets
Familiarity with financial instruments, pricing models, and risk frameworks in credit markets
Experience applying AI/ML techniques to time series, market microstructure, or asset pricing problems
Track record of published research, open-source contributions, or production ML systems What We Offer
Opportunity to work on high-impact, real-world AI applications in finance
Collaborative environment combining top-tier investors, quants, and technologists
Competitive compensation with strong performance incentives
Access to unique datasets and significant computational resources