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.