Machine learning engineer in customer data

ATG has a wealth of data, with millions of guests visiting, eating, and drinking in our theatres every year, and there is a growing need to bring machine learning and data-driven decision-making into our core business processes. The focus of this role is to improve revenue, customer satisfaction, retention, and acquisition by delivering production-ready machine learning solutions and actionable insights from our customer data. You will join a small, growing ML team at a pivotal time for ATG, taking ownership of defined parts of our ML estate, and working towards wider ownership as you grow.

• Take ownership of defined components of the revenue management application (i.e. regional pipelines, seat map and pricing data pulls, or model inference), including their reliability and ongoing improvement.
• Productionise and automate machine learning models end-to-end, including data ingestion, feature engineering, model training and evaluation, inference, and delivery of pricing recommendations.
• Build and maintain data pipelines and MLOps workflows (CI/CD, model versioning, reproducibility, runtime handling) in AWS and Snowflake.
• Support the diagnosis and resolution of production issues, including on-call style triage of failed pipeline runs and data quality problems, escalating to the Lead ML Engineer where needed.
• Design and implement monitoring for model outputs (data drift, prediction drift, anomaly detection) and build dashboards to track model behaviour and recommendation quality daily.
• Implement guardrails for pricing recommendations (thresholds, overrides, fallback logic) in line with agreed designs.
• Write well-tested, documented, maintainable Python and SQL, following team conventions and taking part in peer code review.
• Work with Data Science to take experimental models and prototypes into production, and with commercial teams to translate business needs into technical requirements.
• Document systems, runbooks, and model behaviour so that knowledge is shared across the team.
• Contribute to the design and build of LLM-powered solutions (e.g. internal tools, decision-support systems, automation workflows), including prompt management, versioning, and evaluation.
• Use our data platform to support and automate decision-making across the business, bringing data together to constantly improve our understanding of our products and customers.
• Be an active part of our data community as we build new capability across disciplines, sharing learning and contributing to team standards.

• Proven experience as a machine learning engineer, data scientist, or similar role, with a track record of delivering ML work into production.
• Experience of building and optimising data science workloads in cloud environments (AWS preferred).
• Strong Python and SQL, including writing complex queries and well-structured, tested code.
• Experience deploying and maintaining ML models in production, with a working understanding of MLOps (CI/CD, versioning, reproducibility).
• Experience building monitoring, alerting, and observability for data or ML systems.
• Understanding of system reliability concepts (failures, retries, idempotency, fault tolerance) and an interest in designing safeguards for automated decision systems.
• Able to work effectively with data scientists and data engineers, and to communicate clearly with technical and non-technical colleagues and stakeholders.
• Able to manage your own workload on defined projects, ask for help early, and take on feedback and coaching.
• Будет плюсом: Prior experience of a similar industry (limited capacity and variable demand); Experience with pricing, revenue optimisation, or demand forecasting; Experience building monitoring dashboards (e.g. Streamlit, BI tools); Experience working with LLMs or generative AI systems in a production or applied setting; Experience with Snowflake, Terraform or SageMaker pipelines; Interest in growing towards technical leadership, including mentoring, and owning a wider area of the platform

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