Supporting end‑to‑end project delivery, from analysis through to client engagement and measurable outcomes.
Translating technical data and insights into clear, actionable advice for clients.
Contributing to solutions in areas such as asset management strategies, asset decision-making, performance improvement, system design, and asset condition assessments.
Collaborating with senior advisors on initiatives spanning operational readiness, reliability engineering, productivity improvement, capital portfolio management and complex, multi-disciplinary projects.
Requirements
Experience across the software development life cycle, including containerisation, Git and data science languages/notebooks
Strong data engineering skills: cleansing, transformation, loading and modelling across SQL and NoSQL
Familiarity with major cloud platforms (Azure, AWS or GCP) and their storage, database, data lake and analytics services
Experience with key data and ML tools such as Spark, Databricks, Python, R, TensorFlow/PyTorch and Jupyter/RStudio
Understanding of data visualisation (e.g., Power BI), agile ways of working and holding a relevant scientific or engineering degree