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.