Senior Quantitative Analyst (Machine Learning & Model Validation)

You will operate in a highly collaborative environment where analytics is deeply embedded into decision-making and where models are expected to be robust, scalable, and production-ready.

What You’ll Be Doing:

• Independently validate machine learning models across:
  • Credit risk modelling
  • Customer propensity and behavioural modelling
  • Fraud detection and AML (financial crime) models
• Apply advanced machine learning techniques, including:
  • Supervised learning (XGBoost, CatBoost, Random Forest, Neural Networks)
  • Unsupervised learning (clustering, anomaly detection, isolation forests)
• Manage the full model lifecycle:
  • Feature engineering and data preparation
  • Model training, evaluation, and selection
  • Deployment support and ongoing performance monitoring
• Build, review, and challenge models in Python-based environments using large, complex datasets
• Lead technical discussions and provide mentorship to junior analysts and data scientists
• Collaborate closely with Risk, Technology, and Business stakeholders to ensure alignment
• Ensure models meet governance, performance, and scalability standards across the organisation

What We’re Looking For:

• 6–8+ years’ experience in quantitative analytics, data science, or machine learning
• Strong end-to-end model development experience using Python
• Advanced SQL skills and experience working with large datasets
• Deep experience in techniques such as:
  • Gradient boosting (XGBoost, CatBoost)
  • Neural networks
  • Clustering and anomaly detection
• Experience in credit risk, behavioural analytics, or financial crime modelling
• Exposure to model validation, peer review, or model risk frameworks
• Strong ability to balance technical depth with stakeholder engagement

Qualifications:

• Honours or Master’s degree in:
  Mathematics, Statistics, Computer Science, Actuarial Science, or a related quantitative field

Preferred Experience:

• Experience leading or mentoring data science / ML teams
• Exposure to regulated financial environments
• Cloud-based model deployment experience
• Credit scoring, IFRS analytics, or scorecard modelling exposure
• Familiarity with model governance and validation standards

Why Join?:

• Work on high-impact models used across a major banking environment
• Exposure to a wide variety of modelling applications (not siloed work)
• Strong mentorship from experienced quantitative and risk leaders
• A culture built on simplicity, ownership, and transparency
• Excellent long-term career growth and learning opportunities

Requirements:

• Clear criminal and credit record

• Actuarial (Life, Short‑Term, Pensions, Health, Quant)
• Data Science & Advanced Analytics
• Pricing & Product Modelling
• Market, Credit & Quantitative Risk
• Machine Learning & AI Specialists