Overview
In this role you will join the ACQA Quants team to advance quantitative analytics, automation, and AI-enabled solutions for the Quantitative Investment Strategies (QIS) business. You’ll work at the intersection of quantitative finance, software engineering, and AI, partnering with Trading, Structuring, Sales and Technology to deliver scalable analytics and tools. The role focuses on production-grade analytics, hedge and risk management tools, and intelligent workflows that accelerate QIS innovation. You’ll apply modern AI techniques in a production environment and contribute to lifecycle management of QIS products across asset classes. This is a hands-on, cross-functional role with a clear,
Pay / Benefits
- competitive base salary
- benefits information on benefit site
- flexible working options
- cross-functional collaboration
- global team exposure
- career development opportunities
Responsibilities
- Design, develop, and support quantitative analytics, applications, and automation solutions for QIS
- Collaborate with QIS business to translate requirements into impactful solutions
- Build AI-enabled applications leveraging LLMs, agentic AI architectures, MCP, RAG, and ML techniques
- Develop tools for QIS strategy management, monitoring, and hedging
- Contribute to automation and industrialisation of QIS workflows, calculations, controls, and operational processes
- Collaborate with Quant, Trading, Structuring, Sales, Risk, and Technology teams globally across the full life cycle
Key requirements
- 5+ years in a quantitative analyst, quantitative developer, AI engineering, machine learning, systematic trading, or front-office analytics role
- Bachelor’s, Master’s or PhD in quantitative discipline
- Production-grade analytics, automation, or AI-enabled solutions delivery
- Experience translating business requirements into scalable technical solutions
- Understanding of quantitative finance, derivatives, risk management, and front-office trading workflows
- Strong Python development and production-quality application skills
- Excellent communication and stakeholder management
- strong analytical and problem-solving abilities
- excellent communication and stakeholder management
- ability to translate business requirements into technical solutions
- Python production-quality development
- Software engineering fundamentals (data structures, algorithms, API design, testing, version control)
- Experience with AI/ML in production; LLMs, RAG, agentic AI, MCP, multi-agent systems
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