Senior Quantitative Analyst (Machine Learning & Model Validation)

SUMMARY:
We are partnering with a fast-growing financial services organisation that is building their world-class advanced analytics and quantitative risk capability. Data, machine learning, and model governance sit at the heart of how they design smarter, more inclusive financial solutions.

POSITION INFO:
This is a senior, hands-on technical role focused on the independent validation of high-impact machine learning models across credit risk, financial crime, and advanced behavioural analytics.

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:
In this role, you will take ownership of advanced model validation and quantitative risk analysis:

• 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, and Neural Networks)
  • Unsupervised learning (clustering, anomaly detection, and 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 that models meet governance, performance, and scalability standards across the organisation

• 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 and 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

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

• 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

• 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

• 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