Accounting & Finance

Quantitative Analyst

G Research

London · Greater London · United Kingdom

Overview

In this role you will perform post-trade analysis on large data sets and implement production-grade C# code within a global trading platform. You will collaborate with Research, Engineering and Front Office teams to identify opportunities and improve performance, guided by data-driven insights. You’ll work across medium-term strategic projects and urgent tasks, balancing depth with speed to deliver tangible commercial value. This role sits at the intersection of research and production, powered by scalable compute and a culture of thoughtful experimentation. You will help shape how the platform evolves and scales with the team’s most powerful ideas.

Pay / Benefits
  • Highly competitive compensation
  • annual discretionary bonus
  • Lunch provided
  • 35 days’ annual leave
  • 9% company pension contributions
  • Comprehensive healthcare and life assurance
Responsibilities
  • Review and refine client trading behaviour to uncover opportunities and maximise performance
  • Collaborate with Research, Engineering and Front Office to implement improvements across a global trading platform
  • Perform post-trade analysis in Python using large-scale data and compute
  • Develop and deploy production-grade C# code within the trading platform and monitor changes in real-world use
  • Tackle medium-term strategic projects and short-term urgent tasks with prioritisation and pragmatic delivery for commercial impact
Key requirements
  • Quantitative experience in a fast-paced environment with a proven performance track record
  • Understanding of market microstructure and algorithmic order placement behaviour
  • Strong coding skills in Python and C#, with openness to other languages
  • Ability to challenge status quo and drive improvements
  • Genuine interest in finance and rapid learning ability
  • Attention to detail
  • Strong written and verbal communication
  • Collaborative mindset and cross-functional teamwork
  • Python
  • C#
  • large-scale data analysis

Reference: WJ-747_31160020

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