Advanced Analytics: Machine Learning Engineer at Momentum Metropolitan Holdings Limited

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.

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.

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.

CLIENT

• 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.

PEOPLE

• 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.

FINANCE

• 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, explain ability, 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.

Closing Date    

• 2026/05/29