Advanced Analytics: Machine Learning Engineer MMH

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.

• 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 Sage Maker or Databricks; model registry/experiment tracking (e.g., MLflow).
• Orchestration:
  Airflow / Azure Data Factory / similar.
• CI/CD:
  Git, Git Hub/Git Lab/Azure Dev Ops pipelines.
• Excellent technical and communication skills.
• Knowledge of the financial products applicable, as well as the insurance and retirement fund industry would be advantageous.

• 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, One Lake).
• 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…