The objective of this Master Degree’s Thesis is to contribute to the Dynamic Pricing Tool from EY’s Advisory & Advanced Analytics Department. The contribution consists in two areas: Prediction and Optimization. A product portfolio’s history of prices are used to compute demand predictive models for each product based on prices, to further pass them through an optimization process to maximize the revenue. By utilizing Machine Learning tools as Feature Engineering and stateof-the-art prediction models such as XGBoost, an improvement of the Forecast Accuracy is achieved. Then, by using the already computed predictive models, Simulated Annealing and Broyden-Fletcher-Goldfarb-Shanno (BFGS) non-linearoptimization techniques are applied, to find the best possible prices as input for the previously computed non-parametric prediction models, with the objective of maximizing the revenue. All these concepts are illustrated using a real client’s dataset of beer sales history. The real product names were modified for confidential purposes.