We are working with a leading global investment bank that is actively seeking an Electronic Trading Quant Researcher to join its FX Quant Trading & Research team. The team is responsible for developing cutting-edge electronic trading models across both Spot and Swaps, leveraging advanced statistical analysis, machine learning, and algorithmic research to enhance market-making and execution strategies. This is a front-office role, offering direct exposure to traders, quantitative researchers, and technologists, working on the continuous enhancement of FX eTrading algorithms within a highly collaborative environment. Key Responsibilities: Develop and enhance eTrading algorithms for Spot and Swap FX instruments, improving execution, pricing, and risk management strategies. Apply quantitative research, time-series analysis, and statistical modelling to extract insights and optimise electronic market-making strategies. Work alongside traders to refine fair value models, execution logic, and hedging strategies, with a strong focus on real-time market dynamics. Build and calibrate predictive models using alternative data sources, machine learning techniques, and flow analytics. Implement signal generation techniques to improve price prediction and liquidity capture across a wide range of FX instruments. Collaborate with technology teams to deploy models into production, ensuring performance and scalability within a high-frequency trading environment. Contribute to the continuous refinement of the bank's quantitative research and electronic trading infrastructure. Key Requirements: 2-7 years of experience in quantitative research, algorithmic trading, or eTrading development within a bank, hedge fund, or proprietary trading firm. Strong background in FX markets, particularly electronic Spot and Swaps trading. Hands-on experience with statistical modelling, signal generation, and execution optimisation. Proficiency in Python, R, or kdb/q for research and analysis. Experience coding in a shared codebase and working in a collaborative development environment. Knowledge of Java, C++, or similar low-latency programming languages for algo implementation is highly desirable. Strong understanding of financial market structure, liquidity dynamics, and execution strategies. Excellent communication skills, with the ability to present research ideas and collaborate across teams.