Quant Research Intern – Commodities (12m, London)

Natixis; Greater London; Hybrid; GBP 25,000 - 35,000; Full time

Job summary

Natixis is seeking an Intern - Quantitative Research Commodities (12 months) in London. The role focuses on developing and assessing pricing and risk models for commodities derivatives, with hands-on work in C++ or Python within Front Office pricing architecture.

The candidate will conduct quantitative research for pricing and hedging strategies, stay abreast of numerical methods, and document methodological assumptions and numerical approximations.

Qualifications

• MSc or PhD in a scientific subject.
• Programming skills in C/C++ or C# or Python.
• Knowledge in Financial Markets specifics.
• Knowledge of Murex is a plus.

Responsibilities

• Develop, implement and assess models for pricing and risk of commodities derivatives.
• Develop quantitative research and analysis for financial pricing and hedging strategies.
• Keep abreast of cutting-edge approaches and best practices in numerical simulation and mathematical models.
• Provide support on existing Front Office pricing architecture.
• Systematically document work and provide a clear view of process assumptions and numerical approximations.
• Implement in the C++ Commodities Quantitative Library or Python for trading tools in line with Front Office pricing architecture.
• Document implementations in C++ CQ Library or Python for trading tools.
• Review cutting-edge approaches and best practices in numerical simulation and mathematical models.

Skills

C/C++

Python

Financial markets

Quantitative modeling

Education

MSc/PhD in a scientific subject

Tools

Murex

Job description

Natixis is seeking an Intern - Quantitative Research Commodities (12 months) in London. The role focuses on developing and assessing pricing and risk models for commodities derivatives, with hands-on work in C++ or Python within Front Office pricing architecture.

The candidate will conduct quantitative research for pricing and hedging strategies, stay abreast of numerical methods, and document methodological assumptions and numerical approximations.