Productivity Data Science, Analytics & AI Specialist

Would you like to apply data science and AI to real business challenges across the project lifecycle? Would you like to help shape how work changes through responsible, high‑value AI adoption? Be part of a team that keeps moving. This role sits within the Global FPS (Flexible Pipe Systems) Productivity team and will focus on using advanced analytics, machine learning, and AI to improve decision‑making, reduce cost/value leakage, and support stronger delivery performance.

The role is intended to help move the organisation on its journey to becoming AI‑Native.

• Develop AI agents, prompt‑engineered workflows, and automated solutions tailored to business‑specific use cases, focusing on processes that are slow, repetitive, fragmented, or decision‑heavy, with a clear link to time, quality, risk, insight, or customer benefit.
• Work with teams across the value stream to identify opportunities, surface dependencies, and help shape practical pilots, standards, and scalable approaches.
• Support responsible AI adoption by considering governance, data quality, privacy, consistency, and alignment with wider business and digital standards.
• Help raise the standard of data and AI literacy within the wider team to drive the organisation toward being AI‑native.
• Apply statistical and machine learning techniques to operational, business, and financial data to identify trends, risks, and opportunities across the project lifecycle.
• Design and develop AI‑powered solutions using large language models to support activities such as document analysis, summarisation, insight extraction, and workflow acceleration.
• Build predictive and diagnostic models that help identify margin erosion, cost leakage, delivery risk, and leading indicators of project performance.
• Design and maintain Power BI dashboards and semantic models that combine descriptive analytics with predictive and data science outputs.
• Translate analytical and AI outputs into clear, actionable insight for operational, project, finance, and leadership stakeholders.
• Write clean, maintainable code and build repeatable data pipelines that automate preparation, analysis, and integration into business workflows.

• Strong applied data science experience, including statistical modelling, predictive analytics, and machine learning applied to real business problems.
• Proficiency in Python, SQL, and C#, with experience in data transformation, modelling, automation, and analytical pipeline development.
• Strong Power BI capability, including semantic modelling, DAX, and effective communication of valuable insights through visual reporting.
• Practical experience with generative AI and LLM‑based applications, including prompt design, workflow integration, or custom AI tools.
• Understanding that the value of AI comes not only from the technology itself but from changing how and where work happens.
• Ability to balance experimentation with pragmatism, using evidence to guide decisions and focusing on value over novelty.
• Comfortable working with imperfect or evolving data and helping improve data quality and governance as a foundation for scale.
• Effective communication with both technical and non‑technical stakeholders and collaborative work across functions.

• A curious, pragmatic, and action‑driven approach with confidence to work through ambiguity and discipline to turn ideas into measurable outcomes.
• Experience in project‑based, engineering, manufacturing, industrial, or operational environments.
• Exposure to cost, margin, productivity, or performance data.
• Familiarity with cloud AI or machine learning platforms such as Microsoft Azure AI Foundry, AWS Bedrock, or similar.
• Understanding of data governance, responsible AI principles, and scalable delivery practices.