Anne-Constance S.

In a low-margin sector like collective catering, pricing is a strategic lever for the competitiveness of bids and the profitability of multi-site contracts. Sodexo wanted to structure a global BI product to support its pricing decisions and standardize management practices across countries.

A first version of a BI pricing dashboard was already developed, aimed at helping regional directors, responsible for hundreds of sites, to negotiate their contracts more effectively and manage their margins in a context of inflation and increased pressure on food costs. My role was to manage and scale this Pricing data product.

Objectives

As Product Manager, I managed:

Tools and Data Stack:

Azure DevOps, Databricks, PowerBI, dbt

Responsible for Customer Success for major European accounts, managing AI and Machine Learning projects (NLP classification, sentiment analysis, MMX, MLOps, etc.) with a focus on delivering data-driven products and projects.

Key Achievements:

Clarins wanted to industrialize and secure the production deployment of its machine learning models, which until then had been developed in isolation (notebooks, manual scripts, ad hoc deployments). The challenge was to create a single "ML Factory" platform covering several strategic use cases (real-time e-commerce recommendation, customer segmentation, voice of customer NLP, AI chatbot), while remaining scalable, secure, and cost-controlled.