Manager- Revenue Growth Management

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