Senior Quant/Risk Professional - Machine Learning, Surveillance

Senior Quant/Risk Professional - AI Model Validation, Python, Trade Surveillance sought by leading investment bank based in the city of London.

Inside IR35 -2/3 days a week on site
Summary
This is an exciting opportunity for a highly motivated professional to join a dynamic team focused on validating trade surveillance models. The role involves ensuring that systems used to detect market abuse, insider trading, and other conduct risks are conceptually sound, explainable, and compliant with regulatory standards such as FCA and PRA SS1/23.
The ideal candidate will have a strong quantitative background, hands-on programming experience in Python, and a track record of developing or validating AI/machine learning or statistical models in surveillance or conduct risk contexts.
Key Responsibilities
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 summarising 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
Skills and Experience
Experience in data science, machine learning development or validation, or a quantitative role in financial services or regulated industries
Strong academic background in data science, statistics, mathematics, computer science, or a related field
Solid analytical and problem-solving skills with the ability to assess surveillance systems
Familiarity with configuring, tuning, or validating third-party trade surveillance tools
Understanding of model governance frameworks and regulatory expectations under MAR and FCA
Strong written and verbal communication skills for documenting and presenting findings
Ability to work collaboratively and manage tasks effectively to deliver high-quality outputs
Proven ability to engage with stakeholders across risk, compliance, and technology functions
Please apply within for further details or call on
Alex Reeder
Harvey Nash Finance & Banking

To
From
Record Yes No

Always use these settings

TPBN1_UKTJ