Mathematical Modeller

Organisation Network Rail

Locations

Milton Keynes, UK

Application Deadline

10 days remaining

Benefits

• Flexible/hybrid working arrangements considered.
• Generous annual leave (28 days plus statutory days), with the option to buy/sell days.
• Discounts at stations with your Network Rail pass.
• Defined benefit pension scheme
• 75% subsidy on train tickets.
• Interest-free travel loan for train and car park season tickets.
• Volunteer leave to make a positive impact.

About The Role What does the average day look like?

Every day brings something different. You'll work alongside engineering specialists to understand how railway assets perform, analyse data to uncover insights, and develop models that help shape investment and maintenance decisions across the network.

It's a role that combines problem-solving, collaboration and innovation, with the opportunity to make a real impact on the future of Britain's railway.

A Little Bit About The Team

The Advanced Analytics team sits within the Technical Authority. Amongst other responsibilities, we own and manage the development of whole lifecycle forecasting models of railway assets (such as track, signalling systems and bridges).

These models are used to help plan the spending requirements of future Funding Periods by estimating the impact on asset condition of different funding scenarios.

Next Steps

CV shortlisting will take place after the advert closes, with interviews planned from the week commencing 9th & 16th November , subject to business needs. Early applications are encouraged, as we may close the advert early due to high application volumes.

We encourage you to review our Safety Vision , as safety is an important part of the culture at Network Rail.

All external offers of employment are conditional upon satisfactory completion of pre-employment checks. Click here for more information.

Who are we looking for?

We're looking for a curious and analytical individual with an interest in engineering systems, data, and modelling. The ideal candidate will have experience of working with large datasets, developing model prototypes using coding languages such as Python, R, C# or VBA, and applying statistical techniques to solve complex problems.

You will be able to design and build mathematical models, produce clear and well-structured documentation, and communicate technical methodologies and results effectively to a range of stakeholders, including those with limited modelling knowledge. While experience in all these areas is not essential, a willingness to learn and develop new skills is key.

Essential Criteria

PhD in numerate discipline or equivalent experience e.g. physics, applied mathematics, engineering, statistics and data science

Experience of working in a peer reviewed environment

Experience of working to demanding timescales