Tredence - 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.