Machine Learning Engineer — Production Forecasting & Ops

National Gas Transmission PLC; Warwick; On-site; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Double‑match pension

Annual performance bonus

Salary sacrifice car scheme

Life assurance

Flexible benefits

Wellbeing and development

Job summary

National Gas Transmission PLC is seeking a Machine Learning Engineer to build, deploy and maintain analytics and forecasting solutions for Britain's gas network. You will turn analytical models into robust operational products delivering long‑term business value.

You will collaborate with Data Scientists, engineers and operators to productionise models, monitor performance, and embed solutions into day‑to‑day operations across the network.

Qualifications

• Strong experience as ML/DS/MLOps in production environments.
• Experience deploying forecasting and optimisation models.
• Proficient Python with pandas and scikit-learn.
• Experience with data extraction and SQL.

Responsibilities

• Deploy, monitor and maintain forecasting, optimisation, and ML solutions in production.
• Build model monitoring, automated testing and deployment processes.
• Productionise analytical models with reliability and scalability.
• Analyse large datasets from operational, weather, and market sources.
• Develop forecasting and ML models where required.
• Create dashboards and visualisations to communicate insights.
• Embed solutions into day-to-day operations with engineers and operators.

Skills

MLOps

Python

SQL

Data visualization

Education

Quantitative degree (Data Science, Engineering, Maths, CS)

Master's or PhD in a quantitative field

Tools

Azure ML

Azure DevOps

Pandas

scikit-learn

Job description

National Gas Transmission PLC is seeking a Machine Learning Engineer to build, deploy and maintain analytics and forecasting solutions for Britain's gas network. You will turn analytical models into robust operational products delivering long‑term business value.

You will collaborate with Data Scientists, engineers and operators to productionise models, monitor performance, and embed solutions into day‑to‑day operations across the network.