Manager- Revenue Growth Management

Job description

**Role Overview** Contribute to the design, development, and deployment of price elasticity and promotion optimization models for a global CPG client's RGM transformation program. Work within a team of data scientists to build reusable, market-ready modelling frameworks using retailer POS sell-out data across multiple geographies. This is a practitioner role focused on delivering high-quality, commercially grounded analytical models. You will work closely with RGM SMEs to ensure outputs are operationally relevant and with data engineers to ensure pipelines are model-ready. Key Responsibilities - Build and refine price elasticity models, promotional uplift models, and demand forecasting frameworks using retailer POS/sell-out data/syndicated data/Sell-In data - Build and refine price and promotion optimization models - Contribute to the development of a reusable, parameterized modelling framework that can be deployed across multiple markets with minimal rework - Work with RGM SMEs to translate commercial questions into modelling briefs and validate outputs for commercial sensibility - Collaborate with data engineers to define data schemas, feature requirements, and model-ready dataset specifications - Document modelling assumptions, validation results, and known limitations clearly for both technical and business audiences - Support deployment and handover of models to client teams, including technical documentation and user guides - Proactively flag data quality issues and modelling risks to the project lead **Qualifications Required** - Master's or equivalent in Statistics, Economics, Data Science, Operations Research, or a related quantitative field - 8–14 years in applied data science, with at least 4 years focused on pricing analytics, promotion effectiveness, or revenue management in CPG or Retail - Hands-on experience building price elasticity models and/or promotional lift/uplift & Optimization models in a commercial context - Familiarity with sell-out or POS data from syndicated providers (Nielsen, IRI/Circana) or direct retailer feeds - Proficient in Python (scikit-learn, stats models, XGBoost) and SQL - Experience with cloud analytics platforms (Databricks, Snowflake, or equivalent) - Ability to communicate modelling results clearly to non-technical stakeholders **Qualifications — Preferred** - Experience in CPG industry — understanding of trade spend, promotional calendars, pack-price architecture, and category management - Familiarity with multi-market or multi-geography modelling deployments - Experience building parameterized or templatized model frameworks (as opposed to ad hoc, single-market notebooks) - Understanding of RGM commercial context: how pricing and promotion decisions are made, and how models feed into those decisions - Exposure to MLOps practices: model versioning, monitoring, and retraining workflows