Job Description

Job Purpose

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Apply advanced expertise in machine learning, data mining, analytics engineering, and information retrieval to architect and build highly specialized analytic engines and services. This role focuses on pushing the boundaries of ML innovation, developing enterprise-grade models, and providing expert technical guidance to support strategic business objectives.

Key Responsibilities

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Design and build high-quality, reusable components for business-critical ML models. Apply advanced ML techniques (supervised, unsupervised, deep learning, graph-based approaches) to solve complex business challenges. Conduct exploratory data analysis and feature discovery to identify patterns and valuable insights. Contribute to the feature store lifecycle, including documentation, versioning, governance, and optimisation. Monitor, evaluate, and continuously improve production ML models with a focus on performance, fairness, and ethical AI compliance. Implement experimental design methodologies, hypothesis testing, and rigorous model validation frameworks. Build and maintain scalable, production-grade data pipelines for feature computation, ML training, and deployment. Transform raw data into clean, structured, analytics-ready datasets using tools such as dbt, Airflow, Spark and other modern data engineering frameworks. Engineer and maintain real-time and batch data workflows to support ML and analytics use cases. Implement robust data quality checks, monitoring, and validation processes to ensure data reliability. Optimise pipelines for performance, maintainability, and cost-efficiency across cloud and on-prem environments. Translate complex analytical findings into actionable business insights. Collaborate with business stakeholders to understand requirements and deliver scalable, data-driven solutions. Architect analytical systems aligned with strategic goals and organisational priorities. Contribute to the development and prioritisation of analytics and ML use case roadmaps. Mentor junior data scientists and contribute to quality assurance across the team. Work cross-functionally with data science, engineering, BI, and business teams. Promote ethical and responsible AI practices across all model development and deployment activities. Stay current with developments in ML, analytics engineering, cloud data platforms, and emerging technologies to drive continuous innovation.

Qualifications

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Matric / Grade 12 / National Senior Certificate Advanced Diploma or National First Degree in a relevant field

Bachelor's Degree

in Computer Science, Engineering, Econometrics, Mathematical Statistics, Actuarial Science, or another STEM field

Master's Degree

in the above disciplines Certifications related to

Machine Learning

,

Data Engineering

, or

Cloud ML platforms

Minimum Experience

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7+ years experience in a statistical, data science, or ML engineering role.

Technical & Professional Knowledge

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Strong proficiency in Python (required); experience with R, Scala, or SQL beneficial Hands-on experience with distributed computing tools (e.g., Spark, Ray) Experience working on cloud platforms (AWS, Azure, or GCP) Familiarity with graph databases and analytics (e.g., Neo4j, TigerGraph) Deep understanding of the data science lifecycle and analytics engineering best practices Exposure to feature store platforms, ML model deployment, and MLOps practices Strong communication and collaboration skills * Experience in financial services or regulated environments