RGM Analytics Lead

Job description

Your role: • Own and drive the end-to-end RGM analytics agenda across pricing, price-pack architecture, promotions, trade spend, portfolio mix, bundling, and retailer sell-in pricing. • Build transparent pocket price waterfall / gross-to-net views across list price, invoice price, discounts, rebates, trade terms, promo funding, net price, and margin. • Shape product pricing strategies, price ladders, price-pack architecture, channel packs, retailer-exclusive offers, and bundling opportunities for Personal Health categories. • Use advanced analytics and AI/ML techniques to improve pricing decisions, demand forecasting, price elasticity understanding, promotion ROI, incrementality, cannibalization analysis, and scenario simulations. • Partner with Data Science and Analytics teams to translate RGM problems into model requirements, business rules, test cases, success metrics, and decision workflows. • Support the development of AI-enabled decision tools such as price waterfall cockpits, PPA simulators, promo ROI advisors, bundle simulators, and self-serve insight assistants. • Leverage GenAI and conversational analytics to accelerate insight generation, executive summaries, market signal synthesis, and adoption of self-serve decision support. • Design dashboards, scorecards, and decision-first views in tools such as Power BI, Qlik, Tableau, or similar platforms, ensuring insights are simple, actionable, and business-ready. • Partner with Sales, Marketing, Finance, Category, E-commerce, Analytics, IT, and regional teams to embed RGM recommendations into planning, business reviews, customer discussions, and execution routines. • Build scalable frameworks, governance, guardrails, playbooks, and learning loops to improve consistency of RGM decisions across markets and channels. • Champion responsible, human-in-the-loop use of AI, ensuring transparency, explainability, privacy, business accountability, and appropriate challenge of model outputs. You're the right fit if: • You have 10+ years of experience across Revenue Growth Management, Pricing, Commercial Strategy, Trade Marketing, Sales Finance, Category Management, Commercial Analytics, Data Science, or related roles. • You bring strong RGM fundamentals across pricing, price-pack architecture, promotions, trade spend, portfolio mix, gross-to-net / pocket price waterfall, and retailer or customer profitability. • You have hands-on or close working experience with analytics models in areas such as price elasticity, demand forecasting, promotional uplift, incrementality, optimization, scenario planning, or commercial analytics. • You are comfortable working with large commercial datasets such as sell-in, sell-out / POS, pricing, promotions, trade terms, customer margin, e-commerce pricing, competitor pricing, and market share data. • You can translate business problems into data science requirements, define business logic, guide feature selection, review model outputs, challenge assumptions, and convert findings into commercial action. • You have strong analytical and technical fluency, ideally including advanced Excel, SQL, and exposure to Python or R; experience with Power BI, Qlik, Tableau, or similar BI tools is expected. • You are familiar with AI/ML concepts and know when to use predictive models, optimization, simulations, GenAI, automation, or dashboards for different RGM use cases. • You can communicate complex analytics and AI outputs in a simple, business-relevant way for senior stakeholders and cross-functional teams. • You have experience collaborating with Sales, Marketing, Finance, Analytics, IT, and Data Science teams to drive adoption of tools, models, and new ways of working. • You combine commercial judgment with data-driven thinking and can make recommendations even when data is incomplete or imperfect. Preferred experience: • Experience in consumer goods, consumer health, personal care, beauty, grooming, oral care, small appliances, retail, or e-commerce. • Experience with AI-enabled RGM tools, pricing engines, promotion optimization, trade promotion optimization, scenario simulators, or GenAI copilots. • Exposure to Azure Data Lake, Databricks, Lakehouse architecture, semantic models, data governance, or reusable data products. • Understanding of syndicated market data, retailer portals, POS data, e-commerce price tracking, customer P&L, or competitor pricing datasets. • Bachelors or Master’s degree in Business, Economics, Finance, Marketing, Engineering, Data Science, Statistics, Mathematics, Computer Science, or a related field; MBA or advanced analytics qualification is a plus.