Machine learning Quantitative Engineer London - Hybrid working Rate - £1,200

Key Responsibilities Research, design, and implement machine learning and quantitative models for pricing, trading signals, and risk management across Fixed Income products (rates, credit, FX, mortgages). Apply advanced statistical learning methods (time-series, NLP, deep learning, reinforcement learning, graph-based models) to large-scale, high-frequency, and alternative datasets. Engineer robust data pipelines and real-time model deployment frameworks to support production trading environments. Collaborate with traders, quants, and technologists to prototype and scale strategies from research to execution. Conduct rigorous backtesting, performance analysis, and explainability assessments of machine learning models. Contribute to the development of quantitative libraries and shared research infrastructure. Qualifications & Skills Essential: Strong expertise in machine learning, statistical modelling, and numerical methods with practical applications. Proficiency in Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow) and experience with C++ or Java for high-performance model integration. Solid understanding of Fixed Income products, yield curve modelling, and financial mathematics. Experience building production-level ML systems in low-latency or large-scale environments. Strong communication skills with the ability to interact effectively with both technical and trading stakeholders. Desirable: Previous front-office or systematic trading desk experience. Familiarity with modern MLOps (Docker, Kubernetes, MLflow, Airflow) and distributed computing (Spark, Ray). Experience with alpha signal generation, regime detection, or portfolio optimization. Exposure to alternative/ESG datasets, macroeconomic indicators, and sentiment analysis.