Senior Quant/Risk Professional - Machine Learning

- Independently validate and periodically review trade surveillance models for robustness and regulatory compliance. - Evaluate data quality, feature engineering, and model performance across surveillance systems. - Review model documentation for conceptual soundness, implementation quality, and governance controls. - Conduct benchmarking, backtesting, and stress testing using Python to challenge model design. - Assess statistical and machine learning-based surveillance systems for transparency and effective alert thresholds. - Provide quantitative and qualitative assessments of model accuracy, stability, and business suitability. - Collaborate with model developers, compliance, and surveillance teams to communicate findings and support remediation. - Produce clear and actionable reports summarizing validation outcomes and risk ratings for senior stakeholders. - Support regulatory validation work under FCA and other relevant frameworks. - Contribute to the enhancement of validation methodologies for surveillance models.