Senior Quants: TAG

Job Requisition Details

REQ#142181

Location: Johannesburg, Gauteng

Closing Date: 26 February 2026

Talent Acquisition: Bongiwe Mchunu

Job Family

Career Stream

Leadership Pipeline

FAIS Affected

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

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

Behavioural Competencies