RGM - Data Scientist

About the Company

Contribute to the design, development, and deployment of price elasticity and promotion optimization models for a global CPG client's Revenue Growth Management (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.

About the Role

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.

Responsibilities

• Build and refine price elasticity models, promotional uplift models, and demand forecasting frameworks using retailer POS sell-out data, syndicated data, and 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

• Master's degree or equivalent in Statistics, Economics, Data Science, Operations Research, or a related quantitative field.
• 9–12 years of experience in applied Data Science, with at least 2 years focused on pricing analytics, promotion effectiveness, or revenue management within CPG or Retail.
• Hands-on experience building price elasticity models and/or promotional lift/uplift and optimization models in a commercial environment.
• Familiarity with sell-out or POS data from syndicated providers such as Nielsen, IRI/Circana, or direct retailer feeds.
• Strong proficiency in Python (Scikit-learn, Statsmodels, XGBoost) and SQL.
• Experience with cloud analytics platforms such as Databricks, Snowflake, or equivalent.
• Ability to communicate modelling results effectively to both technical and non-technical stakeholders.

Required Skills

• Experience in the CPG industry with 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 rather than ad hoc single-market solutions.
• Understanding of Revenue Growth Management (RGM) and how pricing and promotion decisions are made within commercial organizations.
• Exposure to MLOps practices including model versioning, monitoring, and retraining workflows.