Skills:

Data Warehousing, Risk Management, Data Analysis, TensorFlow, Big Data

Job type:

Full-time

• Design, develop and deploy advanced machine learning and artificial intelligence models focused on financial risk analytics, credit risk assessment, market risk analysis and fraud detection.
• Lead the end-to-end data science lifecycle, from problem definition and data exploration through model validation, implementation and ongoing performance monitoring.
• Mentor and supervise junior data scientists and analytical staff, fostering a culture of excellence, continuous learning and collaborative problem-solving within the te ...
• Conduct comprehensive exploratory data analysis on large-scale financial datasets to identify patterns, anomalies and opportunities for predictive modelling.
• Develop robust statistical models and econometric frameworks to quantify and forecast financial risks across various asset classes and business segments.
• Create clear and compelling data visualisations and reports that translate complex analytical findings into actionable business recommendations for senior management and risk committees.
• Collaborate closely with risk management, compliance, trading and operations teams to understand business requirements and ensure analytical solutions align with strategic objectives.
• Establish and maintain rigorous model governance frameworks, including validation protocols, backtesting procedures and documentation standards to ensure regulatory compliance.
• Stay abreast of emerging trends, methodologies and technologies in financial AI, machine learning and risk analytics, and evaluate their applicability to our business.
• Optimise data pipelines and analytical infrastructure to enhance processing efficiency, scalability and data quality across analytical systems.
• What we're looking for.
• Master's degree or higher in Data Science, Machine Learning, Statistics, Mathematics, Physics, Computer Science or a related quantitative discipline.
• Minimum 7-10 years of professional experience in data science, machine learning engineering or advanced analytics, with at least 4-5 years specifically focused on financial services, risk analytics or quantitative finance.
• Demonstrated expertise in building and deploying machine learning models in production environments, with proficiency in supervised learning, unsupervised learning, ensemble methods and deep learning techniques.
• Strong proficiency in programming languages such as Python, R or Scala, with experience in relevant libraries and frameworks (e.g. scikit-learn, TensorFlow, PyTorch, XGBoost).
• Solid understanding of financial concepts including credit risk, market risk, operational risk, counterparty risk, regulatory capital requirements and risk measurement methodologies.
• Experience with big data technologies and distributed computing frameworks such as Apache Spark, Hadoop or cloud-based analytics platforms (AWS, GCP, Azure).
• Proven track record of leading analytical projects, mentoring junior staff and driving cross-functional collaboration in a complex organisational environment.
• Strong statistical knowledge including hypothesis testing, causal inference, time series analysis and experimental design.
• Experience with financial data sources, APIs and market data platforms; familiarity with data warehousing and ETL processes is advantageous.
• Excellent communication skills with the ability to present technical concepts clearly to both technical and non-technical audiences.
• Knowledge of financial regulations such as Basel III, IFRS 9, Dodd-Frank or equivalent regional frameworks is highly desirable.
• Certification in machine learning, data science or related disciplines is a plus.