Asset Lifecycle Modeller

Overview

In this role, you help Arcadis advance its asset management capability for rail infrastructure. You’ll contribute to evidence-based investment and maintenance decisions by developing asset lifecycle models and applying analytical tools.

You’ll work with cross-functional teams to deliver high-quality outputs that inform client strategies and asset performance. This is an opportunity to grow as a specialist in digital consultancy, shaping sustainable, long-term value for critical assets.

You join a collaborative, innovation-driven environment with clear development paths and exposure to iconic projects.

Responsibilities

• Support delivery of asset management and lifecycle modelling projects with high-quality analytical outputs
• Develop and apply asset lifecycle models to inform investment, maintenance and renewal decisions
• Utilise analytical and digital tools to interrogate and visualise data, developing models and dashboards
• Contribute to advisory services across asset management, investment planning, and infrastructure strategy
• Build strong relationships with multidisciplinary teams and clients to understand challenges and objectives
• Engage in learning and development activities to enhance technical, analytical, and consulting skills
• Drive innovation by exploring new tools and approaches to improve asset analysis and decision-support

Key requirements

• Degree or expected degree in engineering, asset management, mathematics, data science, economics, physics, operational research or related discipline
• Strong analytical and problem-solving skills with ability to interpret data
• Interest in infrastructure, transport, utilities or built environment
• Ability to communicate quantitative findings clearly in writing and verbally
• Good written and verbal communication skills
• Interest in predictive analytics, digital twins, optimisation or decision-support tools
• Ability to work collaboratively in multidisciplinary teams
• Proactive approach to learning and development
• Strong organisational skills and attention to detail
• Proficiency in Microsoft Excel and other standard data analysis tools
• Ability to translate technical information into client-ready outputs
• Knowledge of whole-life costing, lifecycle planning or investment appraisal concepts
• collaboration
• clear communication
• proactive learning
• Power BI
• Python
• R

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