Markets Quant Analyst: Capital Optimization & Trading

Aplaro Ltd; Greater London; Hybrid; GBP 90,000 - 120,000; Full time

Benefits offered by this job

Structured training

Hybrid work model

Competitive compensation and benefits

Job summary

Citi’s Capital Analytics team in London is seeking a highly quantitative professional to develop cross-asset models and analytics that influence front-office decisions. You will work with traders, structurers, and analysts to deliver scalable Python/C++ tooling for pricing, capital, and risk across global markets.

Ideal candidates hold a Master’s or PhD in a quantitative field and have strong Python/C++ skills, with a deep understanding of statistics and financial modelling.

Qualifications

• Master's degree or PhD in a quantitative field
• Experience building quantitative models in financial markets
• Strong Python and/or C++ programming
• Solid grasp of probability, statistics and numerical methods
• Excellent ability to explain complex concepts to traders and stakeholders

Responsibilities

• Develop cross-asset quantitative models to optimise capital across global trading businesses
• Build analytic frameworks for pricing, balance sheet optimisation, and capital-efficient trade structuring
• Collaborate with traders, structurers and analysts to deliver actionable insights
• Design scalable Python and C++ analytics used across trading, risk, and capital platforms
• Work with large data sets to improve modelling accuracy and performance
• Contribute across the model lifecycle from R&D to production support

Skills

Quantitative modelling

Python

C++

Mathematics

Communication

Education

Master's degree or PhD in Mathematics/Physics/Engineering/CS/Quantitative Finance

Tools

None

Job description

Citi’s Capital Analytics team in London is seeking a highly quantitative professional to develop cross-asset models and analytics that influence front-office decisions. You will work with traders, structurers, and analysts to deliver scalable Python/C++ tooling for pricing, capital, and risk across global markets.

Ideal candidates hold a Master’s or PhD in a quantitative field and have strong Python/C++ skills, with a deep understanding of statistics and financial modelling.