Quantitative Analyst

SUMMARY:

POSITION INFO:
You will be independently validate high‑impact models across credit risk, financial crime, and advanced analytics use cases. This is a hands‑on technical role. What You'll Be Doing Independent validation of machine learning models across: Credit risk models Propensity and behavioural models Financial crime models (fraud and AML) Applying advanced ML techniques, including: Supervised learning (Random Forest, XGBoost, CatBoost, Neural Networks) Unsupervised learning (clustering, isolation forests) Managing model risk across the end‑to‑end model lifecycle, including: Feature engineering and data preparation Model training, evaluation, and selection Production deployment and monitoring Building and reviewing models in Python‑based environments Partnering closely with Risk, Technology, and Business stakeholders Ensuring models meet governance, performance, and scalability standard What We Are Looking For 5-8 years relevant experience Strong hands‑on experience building machine learning and data science models end‑to‑end Proven use of techniques such as: Boosting algorithms (XGBoost, CatBoost) Neural networks Clustering and anomaly detection Advanced proficiency in Python Solid experience with SQL and working with large, complex datasets Experience within credit risk, propensity modelling, or financial crime analytics Experience with independent validation of models and\/or detailed peer review Proven experience researching machine learning model Qualifications Honours or Master's degree in Mathematics, Statistics, Computer Science, Actuarial Science, or a related quantitative fiel Preferred\/ Ideal Experience deploying or supporting models in cloud environments Exposure to credit risk modelling, scorecards, or IFRS‑related analytics Financial crime (fraud or AML) modelling experience Experience designing models with scalability and deployment in mind Familiarity with model risk, governance, or validation standard What's in it for you? Work on high‑impact, real-world models used across the industry. Exposure to a wide variety of models, not just one product or portfolio Learn from experienced quantitative leaders in a collaborative environment Be part of a fast-growing organisation that values simplicity, transparency, and ownership Competitive rewards, learning opportunities, and long-term career growt Conditions of Employment Clear criminal and credit record Should you not receive a response from us within one week of your application, your application has unfortunately not been successful. You can visit our website for more vacancies: \/