Senior Quant/Risk Professional - Machine Learning, Surveillance

Harvey Nash; City Of London; Hybrid; GBP 70,000 - 90,000; Full time

Job summary

A leading investment bank in London is seeking a Senior Quant/Risk Professional to join their dynamic team. The role focuses on validating trade surveillance models and ensuring compliance with regulatory standards. Candidates should have a strong academic background in data science or a related field along with experience in financial services. This position offers a chance to work on critical risk management tasks, driving improvements in surveillance methodologies.

Qualifications

• Experience in a quantitative role in financial services or regulated industries.
• Solid understanding of model governance frameworks and regulatory expectations.
• Ability to assess surveillance systems effectively.

Responsibilities

• Validate trade surveillance models for robustness and regulatory compliance.
• Evaluate data quality, feature engineering, and model performance.
• Collaborate with teams to communicate findings and support remediation.

Skills

Data science

Machine learning development

Analytical skills

Problem-solving

Strong communication skills

Stakeholder engagement

Education

Degree in data science, statistics, mathematics, or related field

Tools

Python

Trade surveillance tools

Job description

Senior Quant/Risk Professional - AI Model Validation, Python, Trade Surveillance

Leading investment bank based in 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, PRA SS1/23.

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 (phone number removed)

Alex Reeder
Harvey Nash Finance & Banking