Quantitative Analyst – Machine Learning & Data Science

About the role

We are seeking a senior Quantitative Analyst to independently validate high‑impact machine learning models across credit risk, propensity, and financial crime use cases. This hands‑on technical position works closely with risk, technology, and business stakeholders to ensure models meet governance, performance, and scalability standards.

Key responsibilities

• Validate machine‑learning models for credit risk, propensity/behavioural scoring, and fraud/AML detection.
• Apply supervised techniques such as Random Forest, XGBoost, CatBoost, and neural networks.
• Utilise unsupervised methods including clustering and isolation forests for anomaly detection.
• Manage the full model lifecycle: feature engineering, data preparation, training, evaluation, selection, deployment, and monitoring.
• Collaborate with cross‑functional teams to align models with business objectives and regulatory requirements.

Required profile

• 5–8 years of experience building end‑to‑end machine‑learning or data‑science models.
• Proven experience in credit risk, propensity modelling, or financial‑crime analytics.
• Honours or Master’s degree in Mathematics, Statistics, Computer Science, Actuarial Science, or a related quantitative field.
• Experience with independent model validation or peer‑review processes.

Required skills

• Python programming
• SQL and large‑scale data handling
• Random Forest, XGBoost, CatBoost
• Neural networks
• Clustering and isolation‑forest techniques
• Feature engineering, model training, evaluation, deployment, and monitoring
• Cloud‑based model deployment (experience preferred)

What we offer

• Work on high‑impact, real‑world models used across the banking industry.
• Exposure to a wide variety of model types and domains.
• Learning from experienced quantitative leaders in a collaborative environment.
• Competitive rewards and long‑term career growth opportunities.