Quantitative Analyst

About the Role

Business and Commercial Banking

Responsibilities

• Acquire and continuously develop/improve deep expertise in banking products, services and risk management.
• Acquire and continuously develop/improve deep domain expertise in quantitative analytics and their application across various risk types and specialised functional.
• Conduct research and analysis to build, maintain and continuously refine mathematical and statistical models and quantitative solutions.
• Leverage mentorship and guidance from Senior Quantitative analysts and the broader quantitative teams regarding best practices and research to calibrate and validate model and quantitative solution components.
• Collaborate with senior quantitative analysts to commercialise quantitative models and embed them into production systems.
• Contribute to the build and maintenance of domain specific models and quant solutions (e.g. credit, pricing, market risk, trading, liquidity, impairments).
• Apply statistical and machine learning techniques to extract insights and improve model performance.
• Ensure accuracy and stability of quantitative solutions and models (and or contributions) while considering the business impact.
• Contribute to testing of components of complex quantitative solutions using multiple testing methods (e.g. back testing, stress testing, unit testing, integration testing, system testing, regression, black box, sanity, conformance, acceptance, forward etc.).
• Analyse quantitative solutions or statistical models (e.g. machine learning, NLP, etc.) outputs using various techniques and methodologies.
• Engineer and validate large datasets for model consumption, ensuring quality and integrity.
• Translate quantitative solutions and model outcomes into actionable business insight and prepare communications (reports, presentations, etc.) tailored to different audiences to enable support and decision making.
• Work within and across quantitative teams to document models, assumptions, methodologies, and other artefacts ensuring validity, reliability and explainability in the commercial and regulatory context within which the organisation operates.
• Produce high-quality model documentation of methodologies, assumptions, and data lineage to support reperformance, audit trail as well as regulatory and internal governance requirements.
• Work and collaborate within and across teams, senior analysts and digital/technology teams to develop, maintain, enhance and deploy scalable models.
• Support senior analysts and broader quantitative teams in the business change management and capability development processes related to the deployment of model/quantitative solutions.
• Ensure agile delivery, cross functional collaboration and deployment readiness of models.
• Participate in multi-disciplinary/cross functional/business teams to design and deploy quant solutions.
• Provide and receive peer reviews and constructive feedback.
• Adopt a development mindset, demonstrating commitment, learning agility and an independent drive for continuous improvement and growth towards independent design and delivery of quantitative solutions. Participate as a member of agile teams (sometimes multiple simultaneously) and deliver within continuously evolving strategic contexts and domain areas.
• Actively contribute to a culture of innovation and continuous learning within the greater quant community.
• Comply with both internal risk governance frameworks and external regulations, ensuring both integrity of quantitative solutions developed and adherence to conduct requirements (e.g., independence between model development and validation, SAS MRM Standards.)
• Develop an understanding of the regulatory requirements and various standards (e.g. IFRS, Basel, etc.), ensuring their effective implementation in all relevant quantitative solutions and model development processes.
• Participate in and provide technical support for the remediation of regulatory, validation, peer review, and audit findings.
• Support model risk mitigation through robust validation, monitoring and remediation practices.

Requirements

• Matric / Grade 12 / National Senior Certificate
• Professional Qualifications/Honour’s Degree
• 0 (Graduate) - 3 Years experience
• Ability to translate mathematical / statistical models and algorithms into code (e.g., Python, R, C++, Java, SQL, SAS, VBA, MATLAB, etc.)
• Ability to independently deliver on tasks in a high-pressure environment.
• Excellent analytical skills, with strong attention to detail.
• Strong communication capabilities including visualisation.
• Ability to translate complexity into simplicity.
• Demonstrates foundational understanding of quantitative methods and apply them to defined tasks; more complex analyses are completed with guidance and supervision.
• Strong knowledge of probability and statistics
• Technical understanding or exposure to statistical analysis and models
• Strong analytical and problem-solving skills and ability to translate complex data and model / solution outcomes into business insights.
• Proficiency or familiarity in programming languages (e.g. Python, R, C++, Java, SQL, SAS, VBA, MATLAB, etc.), with the ability to translate algorithms and mathematical or statistical models into flexible and scalable coding logic
• Technical understanding or exposure to big data concepts, data structures, data storage, data extraction/mining.
• Technical understanding or exposure of model lifecycle management i.e. development, validation, implementation, monitoring.
• Technical understanding and exposure to AI/ML techniques and model explainability tools (e.g., SHAP, LIME etc.).
• Adaptability
• Applied Learning
• Communication
• Continuous Learning
• Continuous Improvement
• Driving for Results
• Influencing
• Business Acumen
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Preferred Qualifications

• Bachelor’s, Honours or Master’s Degree in Mathematics, Statistics, Actuarial Sciences, Physics, Computer Science, Engineering or another highly quantitative field.
• Some experience or exposure in a data driven/analytical environment advantageous.
• Some experience or exposure to any quantitative analytics or related research (even at tertiary level acceptable) is preferred. Exposure to banking regulations, accounting standards and/or financial accounting (e.g. financial statements, financial ratios, budgets, forecasts, interest rate theory, etc) preferred but not essential.
• Some exposure to banking regulation and accounting standards preferred but not essential