A note on experience

This role requires commercially delivered analytical work. We will ask you at interview to walk through a specific project from problem through to business outcome that you owned and delivered in a professional setting. Academic or course-based projects do not qualify. If your experience is primarily from an academic programme, this role is not the right fit at this time

• Partner with supply chain stakeholders to scope problems and translate them into structured, measurable, automated workstreams.
• Build and maintain python/SQL/Snowflake-based data models, dashboards, and self-service analytics for our global operations teams
• Help operationalize AI and agent-based solutions to automate recurring analyses, augment decision support, and supply chain processes.
• Apply statistical methods to inform data driven decisions across our global operations function.
• Manage your projects end-to-end: scoping, stakeholder alignment, data discovery, modelling, validation, and rollout.
• Translate analytical findings into compelling narratives (written, visual, and verbal) tailored to operational, business, and technical audiences.
• Define and track KPIs that measure supply chain performance and flag interventions when metrics drift.
• Contribute to ongoing improvements in data quality, documentation, and analytical standards across the team.

• Programming for analytics: Python or R for modelling, automation, and analytics workflows.
• BI and visualization tools: Tableau, Sigma, Power BI, Looker, or similar — both for building and for design literacy.
• ERP / source systems: Exposure to Oracle Fusion Cloud or similar transactional systems.

• Practical experience applying agentic workflows (e.g., LLM-based assistants, retrieval-augmented analytics, automated reasoning agents) to analytical or operational problems.
• Master's or PhD in a quantitative field (Statistics, Operations Research, Engineering, Physics, Economics, Data Science, Supply Chain, Mathematics, or related).
• Forecasting and optimization: time-series methods as well as other optimization techniques.
• Machine learning fundamentals: classification, regression, clustering, and an understanding of when ML is and isn't the right tool.
• Process improvement methodologies: Lean, Six Sigma, or equivalent structured problem-solving frameworks.
• Stakeholder management and influencing: navigating ambiguity, managing competing priorities, and building trust with non-technical partners.

• Within 3 months: deeply understand our supply chain data landscape, key stakeholders, and top-priority business questions.
• Within 6 months: deliver at least one high-impact analytical product or AI-enabled workflow that changes how a decision is made.
• Within 12 months: become a trusted analytics partner to supply chain stakeholders and a contributor to the broader analytics team's standards and practices.