Senior Commercial Data Analyst, Expereo

• Demonstrable experience working with pricing data in a commercial context — margin analysis, cost benchmarking, win/loss reporting, or equivalent
• Strong proficiency in SQL and Python/R for data extraction, transformation, and analysis
• Experience building dashboards and reporting tools (Power BI, Tableau, MicroStrategy, or similar)
• Rigorous approach to data quality: able to identify anomalies, trace root causes, and implement fixes that stick
• Ability to communicate analytical findings clearly to non-technical commercial and finance stakeholders
• Experience documenting data lineage and metric definitions in a way that scales across teams

Desirable

• We are looking for a commercially minded Data Analyst to join our team, working directly with costing and pricing data at the heart of our quote engine platform.
• This role sits at the intersection of data quality, commercial insight, and pricing performance, giving you direct visibility into the decisions that shape how Expereo wins and grows business globally
• You will be the analytical backbone of the commercial team: owning reporting, managing the data that feeds our predictive models, and building the self-serve dashboards and visualizations that let sales, finance, and commercial stakeholders act on insight without waiting for an analyst.
• Experience with pricing, whether in B2B SaaS, telecoms, logistics, or a similarly complex commercial environment, is strongly preferred and will be central to how you add value from day one
• Produce regular reporting on pricing performance, margin outcomes, and win/loss trends across customer segments and geographies
• Maintain and QA the historical cost dataset that feeds predictive costing models, flagging anomalies and gaps to ensure clean input into the model
• Translate OKR targets into trackable dashboards that give commercial and finance teams clear visibility on progress, ensuring targets are grounded in data and measurable
• Conduct ad hoc analysis to support pricing decisions benchmarking margins by product or region, identifying accounts at risk of churn, or stress-testing proposed price changes
• Build self-serve reporting tools so that sales, commercial, and finance stakeholders can explore pricing and cost data without analyst involvement
• Work with other data scientists to validate model outputs against business intuition and historical benchmarks before they go live
• Support our next-generation product modelling by cataloguing existing product structures, cost components, and pricing rules as a foundation for the new quote engine