Machine Learning Engineer

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

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At National Gas, the work we do matters.

As Britain's national gas network, we help keep the lights on, businesses running, and homes warm by maintaining the critical infrastructure that transports gas across Great Britain. While providing the energy security Britain relies on today, we're also helping transform the network for a clean energy future.

Join us and help secure Britain's energy.

About the role

We're looking for a Machine Learning Engineer to build, deploy, monitor and maintain advanced analytics, forecasting and machine learning solutions that support the safe and efficient operation of Britain's gas transmission network. You'll take ownership of turning analytical models into robust operational products, ensuring they deliver long‑term value to the business.

Data plays a critical role in how National Gas operates and plans for the future. From forecasting network demand and understanding weather impacts to optimising asset performance and operational decisions, you'll help ensure data science solutions are deployed, scalable, maintainable and trusted across the business.

This is an opportunity to work on meaningful challenges with direct impact on national infrastructure. You'll collaborate closely with Data Scientists, engineers, operators and technology teams while applying modern machine learning engineering practices in a production environment.

What you'll be doing

• Deploy, monitor and maintain forecasting, optimisation, machine learning and AI solutions in production environments.
• Build and manage model monitoring, automated testing, deployment processes and operational support capabilities for machine learning and AI solutions.
• Work closely with Data Scientists to productionise analytical models and ensure they remain reliable, scalable, supportable and aligned with operational requirements throughout their lifecycle.
• Analyse and prepare large datasets from operational, asset, weather and market sources.
• Support the development of forecasting, optimisation and machine learning models where required.
• Create dashboards, visualisations and applications that communicate insights to stakeholders.
• Work closely with engineers, operators and business teams to embed solutions into day‑to‑day operations.
• Measure and demonstrate the value delivered by analytical solutions.

What you'll bring

• Strong commercial experience as a Machine Learning Engineer, MLOps Engineer, Data Scientist or similar quantitative role.
• Experience supporting the development, deployment or operation of forecasting, optimisation or machine learning models within a business environment.
• Strong Python programming skills, including experience with libraries such as pandas and scikit‑learn.
• Strong experience deploying, monitoring and maintaining machine learning or analytical solutions in production environments, including automated testing, model monitoring and version control.
• Experience extracting, manipulating and analysing data using SQL.
• Ability to communicate complex technical concepts clearly to both technical and non‑technical stakeholders.
• Degree in a quantitative discipline such as Data Science, Mathematics, Engineering, Statistics, Computer Science or a related STEM subject, or equivalent industry experience.
• Experience with Azure Machine Learning, Azure DevOps or similar cloud platforms.
• Experience working with meteorological or climatological data and models, with the ability to interpret weather and climate drivers, understand data limitations, and apply these insights to forecasting, optimisation and operational decision‑support problems.
• Experience within energy, utilities, infrastructure or other asset‑intensive industries.
• Postgraduate qualifications, including a Master's or PhD in a quantitative discipline.

Inclusive recruitment

We're building a workforce that reflects the communities we serve, championing diversity, and creating an inclusive workplace where everyone is valued for their unique contribution. We support reasonable adjustments throughout the recruitment process and beyond.

National Gas is a Disability Confident employer and signatory of the Armed Forces Covenant.

What we offer

• Market‑leading double‑match pension (up to 12% company contribution)
• Annual performance bonus up to 6%
• Access to the Tusker salary sacrifice car scheme
• 10x salary life assurance and income protection
• Flexible benefits including healthcare, dental, and technology options
• Family‑friendly policies, wellbeing support, and professional development opportunities