Quantitative Analyst

As a Quantitative Analyst specialising in Machine Learning & Data Science you will develop, implement, monitor, and enhance machine learning and advanced analytical models that support strategic decision-making across the Bank. You will apply statistical and machine learning techniques to solve business problems, generate insights from large datasets, and contribute to the development of data-driven products, risk strategies, and customer solutions.

• Develop and maintain machine learning, predictive, and statistical models to address business challenges.
• Analyse large datasets to identify trends, patterns, risks, and opportunities.
• Perform data extraction, preparation, cleansing, and feature engineering activities.
• Apply machine learning techniques including:
  • Decision Trees
  • Random Forest
  • XGBoost
  • Classification Models
  • Regression Models
  • Clustering Techniques
• Evaluate model performance and recommend enhancements.
• Support model deployment and production monitoring activities.
• Conduct model testing and performance validation.
• Develop analytical reports, dashboards, and data visualisations.
• Collaborate with stakeholders to translate business problems into analytical solutions.
• Contribute to model documentation, governance, and audit requirements.
• Stay current with developments in machine learning, artificial intelligence, and advanced analytics.

Minimum:

• 2 to 4 years' experience in a quantitative, data science, machine learning, or advanced analytics role.
• Experience developing, testing, and deploying analytical or machine learning models.
• Experience working with large and complex datasets using Python and SQL.

Ideal:

• Experience within banking, financial services, fintech, telecommunications, or a highly data-driven environment.
• Exposure to credit risk, customer analytics, propensity modelling, fraud analytics, or financial crime analytics.

Essential:

• Strong Python programming skills.
• Strong SQL querying and data manipulation skills.
• Understanding of machine learning methodologies and model evaluation techniques.
• Statistical analysis and hypothesis testing.
• Data wrangling and feature engineering.
• Ability to work with structured and unstructured datasets.
• Data storytelling and presentation skills.
• Strong analytical and problem-solving abilities.