About the role
We are a multi-country 3PL group with subsidiaries across Europe and the US, operating with heterogeneous systems and varying levels of data maturity. The Group Performance team builds reporting and analytical capability across the group, covering customer profitability, commercial pipeline, operational revenue, and transport margin.
You will join a small central team supporting the Performance Manager on group-wide and subsidiary-specific reporting work. The role is hands-on: most of our reporting today runs on Power BI, with data sourced from a mix of ERP exports, CRM, Warehouse Management System (WMS), and SharePoint files. We are at the early stages of modernising how we work, including how we leverage AI tools to be more productive and tackle previously unanalysable data (free-text CRM notes, operational comments, transport documentation).
This is a strong fit for someone who is genuinely curious about applying AI tools to real business problems, but who is also a solid analyst: most of the work is, and will remain, analytical. We care more about how you think than your exact years on paper.
Key responsibilities
Reporting and analytics (the core of the role, ~60-70% of time)
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Build and maintain Power BI reports across group-wide and subsidiary-specific use cases: customer profitability, commercial pipeline (CRM), operational revenue, transport margin.
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Author and review DAX measures and Power Query / M transformations.
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Work with data from a variety of sources (ERP, CRM, WMS, SharePoint, occasional cloud warehouses) and document the resulting models.
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Translate business questions into analytical deliverables, working directly with finance, commercial, and operational stakeholders across subsidiaries.
Ad-hoc analysis
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Support ad-hoc analytical requests from the CFO, CEO, and country teams (margin deep-dives, profitability questions, scenario analyses).
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Bring structure and clarity to questions that arrive as vague briefs.
AI-augmented ways of working
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Use LLM tools as part of daily work: drafting DAX and M, summarising meeting notes, automating repetitive analytical tasks, accelerating documentation.
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Identify opportunities to apply AI tools to specific problems. For example, extracting structured signals from unstructured CRM notes, summarising operational free-text fields, parsing transport invoices, drafting commercial commentary on variance.
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Prototype lightweight AI-powered tools where they bring clear value. We are not building production ML systems; we are building practical tools that save analytical time or unlock previously unanalysable data.
Continuous improvement
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Document and share working methods with the broader finance community across subsidiaries.
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Contribute to discussions on data architecture and tooling as we mature.
