About the role

Turn complex retail and pricing data into clear, actionable business decisions. The role will build and maintain pricing intelligence products, validate pricing recommendations and commercial data, and deliver broader data analysis that improves sales, margin, promotion effectiveness, assortment, customer outcomes, and operational performance. The position partners closely with Commercial, Finance, Operations, Data Science, and BI teams to move from analysis to measurable action.

Key responsibilities

• Classify and maintain SKU-level pricing roles, including Traffic Builder, KVI, EDLP, Promotion, and Normal Margin Builder, and apply Commercial-approved pricing logic.
• Build, maintain, and improve the Price Dashboard, Focus Product Board, Price Index, margin views, and other decision-support tools; ensure agreed data freshness and accuracy.
• Review and validate AI price-decision recommendations before they reach Commercial; identify anomalies, explain key drivers, and recommend appropriate actions.
• Partner with the Data Science team on price elasticity, cross-elasticity, substitution, basket-impact, promotion, and markdown analysis.
• Build margin-mix, price-change, and scenario simulations to support pricing, promotion, and assortment decisions.
• Evaluate price and promotion performance after implementation and translate results into specific recommendations for Commercial and leadership.
• Lead ad-hoc and structured analyses across commercial and operational topics, including sales, customer, category, assortment, margin, productivity, and process performance.
• Develop robust analytical models using SQL, BI tools, Python, and appropriate statistical techniques such as correlation, regression, hypothesis testing, forecasting, or segmentation.
• Own the accuracy of data and logic used in assigned analyses, dashboards, and models; reconcile exceptions with relevant data owners.
• Communicate complex analysis in clear business language and influence stakeholders using evidence, commercial judgment, and practical trade-offs.

Requirements

• Bachelor's degree or higher in Engineering, Economics, Statistics, Business, Finance, Supply Chain, Computer Science, or a related quantitative field.
• 2-5 years of experience in pricing, revenue management, commercial analytics, data analytics, business intelligence, or a related role.
• Strong SQL skills; experience with Power BI or another BI tool. Python and Databricks SQL are preferred.
• Ability to analyze large datasets, validate data quality, build repeatable analytical models, and present findings to business stakeholders.
• Working knowledge of pricing, price elasticity, promotion effectiveness, margin, or retail commercial concepts; retail/FMCG experience is preferred.
• Knowledge of basic statistical methods, including hypothesis testing, correlation, and regression; experience with forecasting or experimentation is an advantage.
• Strong business-partnering, problem-solving, presentation, communication, and influencing skills.
• Able to work independently, manage multiple priorities, and deliver under time pressure with high attention to detail.