Job Purpose
- To analyse and model complex customer transactional dynamics, unlocking deep, data-driven insights into financial behaviours, needs, and preferences. This role transforms high-dimensional datasets into actionable strategic intelligence, empowering the business to enhance customer value and drive targeted, high-impact interventions through evidence-based decision-making.
Job Responsibilities
Customer & Transactional Analytics:
- Analyse customer transactional and behavioural data to uncover trends, drivers, and opportunities that support strategic decision‑making.
- Develop, refine, and interpret performance analytics to monitor customer and business outcomes within defined risk appetite.
- Conduct deep‑dive investigations to understand emerging customer behaviour patterns and advise business partners accordingly.
Insight Generation & Strategic Advisory
- Translate complex analytical findings into clear, actionable insights for stakeholders across Personal Banking.
- Provide data‑driven recommendations that inform customer strategies, product enhancements, targeted interventions, and operational decisions.
- Present insights and analytical outputs to leadership forums in a structured and compelling manner.
Ensure Big Data translates to Business Value, by:
- Translating business needs into data use cases with clear hypotheses, success criteria, and value metrics
- Distil signal from noise by identifying high‑value data elements/features, ensuring quality, lineage, and responsible data use.
- Build, operationalise and drive adoption of decision-grade data, analytics and tools (e.g. dashboards, models, segmentations, decisioning)
Model & Solution Support
- Support model development by validating behavioural assumptions, assessing data quality, and conducting peer reviews.
- Challenge and influence model-building methodologies and customer strategies to ensure best practices and value delivery.
- Partner with systems, strategy, and product teams to ensure that analytical insights are embedded into solutions and decision-making processes.
Reporting & Performance Monitoring
- Build, automate, and enhance reporting frameworks that track key behavioural, customer, and performance metrics. That is, ensure we can track value and report on solutions generated by the team.
- Identify anomalies or shifts in customer behaviour and proactively escalate risks or opportunities.
Research and introduce new technologies and innovations that drive profitability or efficiency, like:
- Improved modelling approaches and capabilities,
- Enhanced optimisation techniques.
- Research, prototype, and introduce new technologies.
- Hypothesis‑driven experimentation.
- Perform horizon scanning & scouting to track emerging tech, open‑source projects, vendor roadmaps, and academic research.
Stakeholder Engagement & Cross‑Functional Collaboration
- Build strong relationships with business, operations, product, and risk partners to influence decision‑making.
- Manage stakeholder expectations throughout analytical, model, or solution development cycles.
- Communicate findings clearly across both technical and non‑technical audiences.
Culture, Learning & Organisational Contribution
- Contribute to a culture of excellence, innovation, and transformation by actively participating in organisational and team initiatives.
- Support junior analysts through coaching, code reviews, and technical guidance.
- Share knowledge, mentor colleagues, and stay current with industry trends, analytical methods, and behavioural science insights.
- Support corporate responsibility and sustainability initiatives in areas of influence.
Professional Exposure
The ideal candidate will have practical, hands-on exposure to:
- Software Engineering / Coding Fundamentals: Solid grounding in computer science/coding principles, including Object-Oriented Programming (OOP), design patterns, data structures, and algorithmic complexity (Big-O).
- Distributed Computing & Big Data: Working with large-scale data processing systems and distributed environments.
- Modern DevOps Integration: Active usage of CI/CD pipelines, version control (Git), and containerisation technologies (Docker/Kubernetes) within a microservices or API-driven architecture.
- Deep Learning & Optimisation: Proficiency with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and application of continuous/discrete mathematical optimisation techniques.
- Model Governance: Productionising models with rigorous tracking, specific versioning, and governance using tools such as MLFlow.
Professional Knowledge
Core Programming & Engineering
- Expert Proficiency: Advanced Python skills with deep knowledge of ML ecosystems (TensorFlow, PyTorch, Scikit-learn).
- Computer Science Fundamentals: Mastery of Object-Oriented Programming (OOP) patterns, data structures, algorithms, and complexity analysis (Big-O).
- Polyglot Advantage: Exposure to performance-aligned languages such as Java, C++, Go, or Rust is advantageous (though not required).
Data, MLOps & Infrastructure
- Big Data Ecosystems: Strong command of distributed data systems (SQL, Spark) and cloud-native data tooling.
- MLOps Architecture: Practical knowledge of model lifecycle management (MLFlow), containerisation (Docker/Kubernetes), CI/CD pipelines, and API integration.
- Data Strategy: Expertise in designing feature stores, high-performance feature engineering, and managing the end-to-end data lifecycle.
Mathematical & Domain Expertise
- Theoretical Depth: Solid grasp of vector calculus, linear algebra, probability theory, statistical inference, and mathematical optimisation.
- Governance & Risk: Understanding of model governance, regulatory modelling standards, and frameworks specific to credit or risk modelling.
Behavioural Competencies
- Innovative & Curious: A relentless learner who stays ahead of the curve, passionate about applying emerging technologies and modern analytical approaches to solve old problems.
- Analytical Problem Solver: Possesses the intellect to deconstruct complex, ambiguous modelling challenges into scalable, logical solutions.
- Collaborative Powerhouse: A cross-functional partner who drives impact through strong stakeholder management, capable of delivering results individually or by influencing others.
- Resilient & Adaptable: Thrives in rapidly evolving environments; comfortable with ambiguity and quick to pivot strategies when business needs change.
- Technical Communicator: Translates dense technical concepts into clear, actionable insights for non-technical leadership.
- Owner's Mindset: Takes full accountability for the end-to-end delivery and reliability of modelling solutions.
- Force Multiplier: Demonstrates a coaching mindset, actively mentoring junior analysts to uplift the team's overall technical capability.
Essential Qualifications - NQF Level
- Matric / Grade 12 / National Senior Certificate
- Professional Qualifications/Honour’s Degree
Qualification
Minimum Requirements
- Honours Degree in a quantitative or technical discipline, like Computer Science, Engineering (Industrial, Electrical, Computer), Mathematics/Applied Mathematics, Statistics, or Computational/Theoretical Physics.
Preferred
- Master’s Degree (or higher) in a related quantitative field
Minimum Experience Level
- 5-8 years of core experience in quantitative modelling, data science, or advanced analytics.
- Production Engineering: Demonstrated ability to write robust, modular, and well-structured Python code for production environments.
- Domain Expertise: Proven track record in building and deploying machine learning models, with specific experience in Credit Risk or financial modelling being highly advantageous.
- Agile Delivery: Experience working within Agile data science or engineering squads.
Technical / Professional Knowledge
- Industry trends
- Microsoft Office
- Principles of project management
- Relevant regulatory knowledge
- Relevant software and systems knowledge
- Risk management process and frameworks
- Business writing skills
- Microsoft Excel
- Business Acumen
- Quantitative Skills
Behavioural Competencies
- Applied Learning
- Coaching
- Communication
- Collaborating
- Decision Making
- Continuous Improvement
- Quality Orientation
- Technical/Professional Knowledge and Skills
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