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
4–14 years in applied data science, with at least 2 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