Advanced Analytics: Machine Learning Engineer

Job Title: Advanced Analytics: Machine Learning Engineer Company: Momentum Corporate Job Category: Machine Learning Roles & Responsibilities: Role Purpose Drive the productionisation, operationalisation, and lifecycle management of advanced analytics and machine learning models within Momentum Corporate, with the appropriate incorporation of AI\-based models. The role transforms actuarial and data science prototypes into robust, scalable, auditable production systems in a regulated insurance context \- partnering closely with Actuarial, Data Science, and IT to ensure reliability, explainability, and value at scale. Duties \& Responsibilities INTERNAL PROCESS * Actively participates in the design, build and deployment of the solution to a set problem statement by applying advanced analytics, with a particular focus on: * Driving the transformation of actuarial and data science prototypes into production\-grade services (batch jobs and/or APIs), including packaging, testing, documentation, and release. * For mature projects and data pipelines establish Continuous Integration or Continuous Delivery (CI/CD) for ML including scalable, automated scoring pipelines and selecting fit\-for\-purpose architectures (scheduled batch vs. real\-time API). * For new/emerging projects and data pipelines establish tactical data pipelines where appropriate. * Design, deploy, and operate ML pipelines that can leverage Microsoft Fabric artefacts developed by actuarial and other business facing specialists (Lakehouse, Notebooks, Data Pipelines, Warehouse, OneLake). * Partner with Data Engineering and IT to ensure reliable, well\-documented data inputs for training, scoring, and monitoring. * Collaborate with specialists in IT to align on consistent standards and hybrid solutions. * Support the use of machine learning techniques or tools to select features, create and optimise models. Where relevant, aid in the preprocessing of structured and unstructured data, including AI\-based techniques. * Prepare pipelines and deployment patterns that can integrate or leverage off of AI/LLM components (retrieval, orchestration, evaluation) where relevant. * Standardise repository structures, coding conventions, and templates for reusable model components and jobs. * Strategic implementation of ML and AI\-based solutions to enhance model efficiency. * Promote reusability, maintainability, reliability, and scalability in design and development of model solutions. * Embed governance by design: model cards, validation evidence, performance baselines, approvals and sign\-offs for regulated use. * Ensure explainability and auditability of production solutions (feature attributions/diagnostics appropriate to the model class and use case). * Act as the technical interface to IT for environments, network/security patterns, deployment pathways, and operational readiness. * Collaborate closely with actuaries and data scientists to production\-align model design (latency, throughput, cost, explainability, stability). * Provide technical enablement to the analytics team (templates, how\-to guides, code reviews) to uplift production\-ready practices. * Design for cost efficiency across compute, storage, and orchestration; monitor runtime costs and optimise resource utilisation. * Support model performance by catering for monitoring data drift and re\-training systems. * Recommend and implement MLOps tooling (experiment tracking, model registry, orchestration, monitoring) and evolve standards across the team. * Keep current with platform capabilities (e.g. Azure ML vs AWS vs Databricks, containerisation, orchestration services) and introduce pragmatic improvements that reduce risk and lead time. * Collaborating with cross\-functional teams: build and maintain relationships with clients and stakeholders that promote cross\-delivery practice solutions. * Maintain strong relationships with Actuarial, Data Science, IT, and business owners to deliver reliable production ML services that meet agreed SLAs/SLOs. * Represent Advanced Analytics in architecture, security, and governance forums to ensure compliant deployment patterns. * Continuously improve turnaround times, stability, and observability of ML services; proactively communicate incidents and remediation. * Champion solution explainability and auditability to support business adoption and regulatory expectations. * Establish and socialise engineering standards for model packaging, testing, observability, and documentation. * Operate effectively in a lean, business\-embedded advanced analytics team, balancing tactical delivery with the progressive maturation of engineering practices. * Coach actuaries and data scientists on production\-ready coding practices (data contracts, dependency management, testing, reproducibility). * Contribute to a culture of continuous improvement and incident learning (post\-mortems, blameless RCA, playbooks). * Participate in a positive work climate and culture to energise employees and give meaning to work. * Promote a culture that guides and directs best practice, fostering an environment of continuous learning, improvement and cohesiveness. * Participate in a learning and growth culture whereby information regarding successes, issues, trends and ideas are actively shared within Momentum Corporate but also at Group level, through participation in Group\-wide Forums * Participate in performance management frameworks in line with Momentum Corporate’s guidelines. * Encourage innovation, change agility and collaboration within the team. * Design for cost efficiency across compute, storage, and orchestration; monitor and optimise runtime costs of pipelines and model endpoints. * Implement controls that reduce operational and compliance risk (access, secrets, data minimisation, retention). * Track and report service reliability metrics (uptime, latency, failure rates) and cost KPIs relevant to production ML operations. * Optimally contribute to the budget design for the area, including the motivation for expenditures and implementation of financial regulations. * Follow risk management, governance, and compliance policies in own practice area, to identify and manage risk exposure liability. Competencies * End\-to\-End Ownership \& Delivery: Takes accountability for production ML services from deployment through operation, monitoring, and continuous improvement. * Systems Thinking \& Engineering Rigor: Designs solutions with reliability, observability, scalability, and full lifecycle management in mind. * Pragmatic Problem\-solving: Favors simple, robust, and maintainable solutions that balance technical excellence, risk, maturity of business area and business value. * Collaboration \& Technical Influence: Effectively bridges Actuarial, Data Science, IT, and business stakeholders through clear, credible technical communication. * Governance \& Compliance Mindset: Embeds auditability, explainability, security, and regulatory compliance by design in all production solutions. * Continuous Improvement \& Innovation: Drives automation, learning from incidents (post\-mortems), and incremental innovation to improve reliability and speed to value. * Business \& Stakeholder Acumen: Demonstrates understanding of the group insurance environment and a strong commitment to stakeholder outcomes and service excellence. * Professional Impact \& Team Contribution: Shows self\-awareness, inclusiveness, and a collaborative mindset; contributes to growing team capability and supporting change. Work Location: Cape Town, WC, South Africa Availability: Full time

Requirements Experience and Qualifications * Degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field (or equivalent experience). * 3\+ years’ experience in ML engineering, production data science, or platform engineering, including hands\-on deployment and operation of ML models in production. * 2\+ years’ experience in regulated environments (preferably insurance/financial services): audit trails, explainability, approvals, data protection. * Demonstrable experience with model CI/CD, packaging (containers/conda), model \& data versioning, and monitoring/alerting. * Experience building batch scoring pipelines (e.g., orchestrated jobs) and, where applicable, real\-time inference (APIs, message queues). * Experience operating production ML workloads with appropriate monitoring, logging, alerting, and runtime optimisation. * Proficiency in Python and SQL.ML libraries: scikit\-learn; familiarity with PyTorch and/or Keras (nice to have). * Cloud ML platforms: Azure ML, AWS SageMaker or Databricks; model registry/experiment tracking (e.g., MLflow). * Orchestration: Airflow / Azure Data Factory / similar. * CI/CD: Git, GitHub/GitLab/Azure DevOps pipelines. * Excellent technical and communication skills. * Knowledge of the financial products applicable, as well as the insurance and retirement fund industry would be advantageous. Key Skills: Machine Learning, Python, PyTorch Salary: Not disclosed