Senior Quantitative Analyst

Job Requisition Details

REQ#142177

Location: Johannesburg, Gauteng

Closing Date: 25 February 2026

Talent Acquisition: Bongiwe Mchunu

Job Family

Career Stream

Leadership Pipeline

FAIS Affected

Job Purpose

We are seeking a creative and technically adaptable candidate to design, develop, and automate next-generation credit and customer toolkits. This role focuses on building scalable data and modelling pipelines that enable real-time deployment and high-impact decision-making.

Leveraging strong quantitative computing and machine learning skills, the incumbent will drive innovation and enhance predictive performance across key portfolios.

Crucially, this role champions the use of Agentic AI with a "human-in-the-loop" approach to accelerate productivity and redefine the model lifecycle.

Job Responsibilities

• Advanced Data Engineering & Pipeline Architecture
  • Develop & Optimise: Build robust analytics data pipelines using Python, Spark, Airflow, and distributed computing frameworks (containerised).
  • Automate & Scale: Design reusable analytical components with capabilities for automated feature engineering and feature store population to ensure solution consistency and scalability.
• MLOps & Model Lifecycle Management
  • Operational Excellence: Implement best‑practice MLOps methodologies, including CI/CD, containerisation, and cloud‑ready model operations.
  • End-to-End Tracking: Orchestrate modelling pipelines using tools like MLFlow for comprehensive tracking, versioning, and deployment.
  • Real-Time Architecture: Design and deploy real-time model architectures with embedded automated monitoring capabilities to ensure reliability.
• Driving Business Value & Strategy
  • Signal from Noise: Distil high‑value data elements from Big Data to build and operationalise decision-grade insights, analytics and tools.
  • Solution Delivery: Translate business requirements into clear hypotheses and use cases with defined success criteria and value metrics.
  • Performance Monitoring: Track the value of deployed solutions, reporting on ROI while proactively identifying anomalies or areas for improvement.
• Innovation & Future-Proofing
  • Hypothesis-Driven Experimentation: Research, prototype, and introduce new technologies, such as enhanced optimisation techniques, to drive profitability and efficiency.
  • Tech Radar: Actively monitor emerging technologies, open‑source projects, and academic research to keep the stack cutting-edge.
• Stakeholder Engagement & Culture
  • Cross-Functional Partnership: Build strong relationships with business, operations, product, and risk partners to influence decision‑making and manage expectations throughout the development cycle.
  • Communication: Articulate complex technical findings clearly to both technical and non‑technical audiences.
  • Mentorship & Growth: Foster a culture of excellence by coaching junior analysts, conducting code reviews, and sharing knowledge on industry trends.

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.

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.

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