FX/Rates E-Trading Quant

Overview

As an e-trading Quant in UBS's electronic trading team, you will develop and back-test algorithmic trading strategies for FX and interest rate derivatives. You’ll work closely with traders and engineers to deploy and monitor strategies in production, using data-driven methods to optimise pricing, risk, and execution. This role offers exposure to real-time trading, PnL analysis, and ongoing model refinement in a fast-paced, collaborative environment. You’ll contribute to innovation in automated trading and drive revenue through improved tools and methods.

Pay / Benefits

• flexible working options
• inclusive culture
• opportunity to grow
• supportive team
• AI-driven optimization
• global exposure

Responsibilities

• Design, back-test, and implement algorithmic trading strategies for FX & interest rate markets
• Apply statistical analysis and machine learning to optimise pricing, quoting, and automated risk management
• Involves full lifecycle from research and model development to coding, deployment, and real-time monitoring
• Monitor strategy performance, PnL, and risk exposures; use insights to identify enhancements
• Collaborate with senior traders, software engineers, and quants to integrate new algorithms into the e-trading platform
• Suggest improvements to trading tools, frameworks, and processes to drive revenue and efficiency
• Engage in cross-functional work in a front-office, high-tech trading environment

Key requirements

• Degree in a quantitative field
• Strong programming skills with data analysis and scientific libraries; ability to write clean, efficient code
• Technical aptitude with data-driven decision making; comfort with large datasets and statistics
• Self-starter capable of independent work and owning deliverables in trading
• Strong communication and interpersonal skills; ability to explain complex problems to technical and non-technical stakeholders
• Team player with collaborative mindset and ability to build relationships
• Prior exposure to FX, rates, or STIR markets is advantageous but not required
• Experience with electronic trading systems or market data infrastructure (order book dynamics, FIX protocol, KDB/time-series databases)
• Software engineering practices in a professional environment (version control, testing, code reviews, DevOps/CI-CD); knowledge of Java or C++ for low-latency systems preferred
• Curiosity about applying AI to improve workflows with sound risk and policy alignment

• communication
• teamwork
• self-motivation
• statistical analysis
• machine learning techniques
• data handling of large datasets

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