Lead Quantitative Analyst

SUMMARY:
Our client is looking for a Lead Quantitative Analyst to join their collaborative and fast-growing team.

POSITION INFO:
Quantitative Analyst Lead
We’re looking for a Lead Machine Learning and Data Science Analyst to 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

• Leading the 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
• Leading and mentoring analysts and junior data scientists
• Partnering closely with Risk, Technology, and Business stakeholders
• Ensuring models meet governance, performance, and scalability standards

• 6–8+ years relevant experience, with demonstrated technical leadership
• 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
• Ability to lead technically while remaining actively involved in modelling work
• 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 models

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

• Experience leading or building ML teams in a regulated environment
• 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 standards

• 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 growth

• Clear criminal and credit record