Senior Machine Learning Engineer

Overview

In this role you lead data science and MLOps initiatives at Global, driving production-ready ML solutions for cross-functional teams. You will translate insights into scalable products for the DAX platform and other business streams, influencing commercial outcomes. You’ll mentor colleagues, collaborate with engineering and analytics teams, and shape how data informs decision making across advertising domains. This is an opportunity to own end-to-end data products in a fast-paced, collaborative environment.

Responsibilities

• Model development and analytics to improve internal processes and propose new products
• Prototype and evaluate machine learning algorithms used across Global
• Present insights to technical and non-technical stakeholders
• Build and maintain scalable pipelines for training, validation, deployment and monitoring
• Refactor legacy models for production use (performance, reliability, cost-efficiency)
• Automate model lifecycle management (retraining, versioning, monitoring)
• Collaborate with data/analytics engineering to deploy models into production
• Share MLOps best practices and ensure consistent deployment across projects
• Lead cross-functional data science projects end-to-end
• Mentor junior team members
• Stakeholder management to maximise business impact
• Explore new data science products for the DAX platform (targeting, measurement, consumer analytics)
• Stay adaptable and contribute to team spirit and collaboration

Key requirements

• Demonstrable understanding and experience in predictive, prescriptive and generative ML/AI methodologies
• Strong degree in a numerate subject
• Expertise in Python and SQL
• Experience with MLOps platforms (e.g., MLflow, Snowflake intelligence) and CI/CD pipelines
• Extensive experience deploying, monitoring and maintaining ML/AI models in production
• Strong understanding of model performance monitoring, logging, and alerting frameworks
• Knowledge of software engineering best practices (version control, testing, code reviews)
• Attention to detail under pressure and ability to deliver high-quality work on tight timelines
• Professional manner with ability to interact with senior management
• Ability to lead and mentor others within and beyond the immediate team
• Ability to explain analysis to non-numerate audiences and empathise with their perspective
• Ability to build and maintain relationships with a diverse audience
• Can-do attitude and aptitude for translating data challenges into solutions
• Commitment to inclusive culture and collaboration

• Stakeholder management
• Mentoring and coaching
• Cross-functional collaboration
• Python
• SQL
• MLOps platforms (MLflow, Snowflake intelligence)

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