Productivity Data Science, Analytics & AI Specialist

As the Productivity Data Science, Analytics & AI Specialist, you will develop applied data science and AI solutions that address real operational and financial challenges across the business. You will work across functions to identify high-value use cases, build trusted analytical tools, and support responsible adoption of AI in ways that improve outcomes. As a Productivity Data Science, Analytics & AI Specialist, you will be responsible for:

• Develop AI agents, prompt-engineered workflows and automated solutions tailored to business-specific use cases.
• Focusing on business-led use cases that solve slow, repetitive, fragmented, or decision-heavy processes, with a clear link to time, quality, risk, insight, or customer benefit.
• Working with teams across the value stream to identify opportunities, surface dependencies, and help shape practical pilots, standards, and scalable approaches.
• Supporting responsible AI adoption by considering governance, data quality, privacy, consistency, and alignment with wider business and digital standards. As part of this, you'll help raise the standard of data and AI literacy within the wider team to drive the business on it's journey to be AI-native.
• Applying statistical and machine learning techniques to operational, business and financial data to identify trends, risks, and opportunities across the project lifecycle.
• Designing and developing AI-powered solutions using large language models to support activities such as document analysis, summarisation, insight extraction, and workflow acceleration.
• Building 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.
• Translating analytical and AI outputs into clear, actionable insight for operational, project, finance, and leadership stakeholders.

• Writing clean, maintainable code and building repeatable data pipelines that automate preparation, analysis, and integration into business workflows.
• Have strong applied data science experience, including statistical modelling, predictive analytics, and machine learning applied to real business problems.
• Be proficient in Python, SQL and C#, with experience in data transformation, modelling, automation, and analytical pipeline development.
• Bring strong Power BI capability, including semantic modelling, DAX, and effective communication of worthwhile insights through visual reporting.
• Have practical experience with generative AI and LLM-based applications, including prompt design, workflow integration, or custom AI tools.
• Understand that the value of AI comes not only from the technology itself, but from changing how and where work happens.
• Be able to balance experimentation with pragmatism, using evidence to guide decisions and focusing on value over novelty.
• Be comfortable working with imperfect or evolving data and helping improve data quality and governance as a foundation for scale.
• Communicate effectively with both technical and non-technical stakeholders and work collaboratively across functions., A curious, pragmatic, and action-driven approach, with the confidence to work through ambiguity and the 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.