Javier Díaz Cortés

Department

Data Science

Job Title

Data Science Manager

INSTRUCTION LANGUAGES

English

PERSONAL BIO

Javier Díaz Cortés is Head of AI Research at Inditex Technology. He is focused on developing new ML and AI solutions that are applied over a wide variety of processes across the company. Over the last years he has developed several algorithms of different kinds, predictive models, computer vision or recommender systems that are integrated in different applications. He also holds a PhD in nuclear physics from the University of Santiago de Compostela (Spain) where he studied nucleon-nucleon interactions in nucleus-nucleus collisions.

WORK EXPERIENCE AND AWARDS

Data Science Manager, Inditex, Spain (2022-present)
Senior Data Scientist, Inditex, Spain (2020-2022)
Data Scientist, Accenture, Spain (2019-2020)

EDUCATIONAL BACKGROUND

PhD in Nuclear Physics, University of Santiago de Compostela, Spain
Master in Nuclear Physics, University of Santiago de Compostela, Spain
Bachelor in Physics, University of Santiago de Compostela, Spain

VacancIES

Brief Introduction of the Department

At Inditex, the Data Science and AI team drives innovation by harnessing the power of advanced analytics and artificial intelligence to enhance decision-making across the business. The team analyzes large-scale datasets to uncover patterns, predict trends, and optimize key processes. Using cutting-edge machine learning and AI techniques, the team develops algorithms to solve complex challenges. Collaboration with cross-functional teams ensures our solutions are tailored to real-world needs. The team's goal is to seamlessly integrate data and AI into every aspect of the business, delivering impactful insights and driving sustainable growth.

Department Staff Number

More than 10 people

Internship Job Description

Understanding causal relationships in retail is essential for making strategic decisions. For instance, introducing a new product can lead to cannibalization, where the sales of existing products decline, or adjusting the price of a product might alter its demand and impact market dynamics. However, these effects often remain obscured by complex interactions between variables and temporal patterns, making it challenging to identify their true causes and predict their outcomes accurately.

This project focuses on developing advanced artificial intelligence (AI) algorithms to explicitly establish causal relationships between such events. Using state-of-the-art techniques in causal inference and deep learning, we aim to uncover the hidden connections between retail actions (e.g., product launches, price changes) and their effects on sales and demand.

Our goal is to build predictive models that not only estimate these impacts but also provide actionable insights by simulating counterfactual scenarios, helping businesses plan better. Additionally, we will design tailored Key Performance Indicators (KPIs) to quantify critical dynamics like cannibalization and price elasticity of demand, ensuring retail decision-makers have practical tools for evaluating their strategies. This project seeks to provide a scalable, data-driven framework to optimize inventory management, pricing strategies, and overall revenue performance.