Ines Amor

Ines Amor develops predictive models for pricing, forecasting, and the analysis of purchasing behavior. She leads projects focused on explainable AI and optimization algorithms designed for retail pricing decisions.

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For our clients, Inès configures the AI engine at the heart of the platform:

• Designs and calibrates sales forecast and price elasticity models (simple and cross-price) for each catalog.
• Configure pricing recommendations based on the retailer's business constraints (price image, price tiers).
• Document the factors behind each recommendation so that the pricing teams can maintain control over the decision.
• Works with clients' data teams to ensure the reliability of input data (sales, inventory, competition).