Inovretail
Pedro Couto Soares has a diverse work experience in the IT industry. Pedro started their career at Sonae as an IT Consultant, where they worked as a system analyst on various Data Warehousing projects, including Oracle upgrades and the implementation of new data domains. Pedro then moved on to Wm Morrison Supermarkets Plc as an IT Consultant, where they successfully implemented Sales, Loss and Prevention, Staff Discount, and Items and Stores solutions. Pedro also worked as an IT Consultant on the EVOLVE Program, leading the analysis, design, and implementation of a Data Warehouse solution for Sonae. At WINGS Program @ WE Fashion, Pedro's role focused on the integration part to ORDW and all the interfaces involving Oracle Warehouse Builder. Pedro then joined the IRIS Program @ Ahold Europe as an IT Consultant, leading and managing Data Warehousing and Business Intelligence projects. Most recently, Pedro worked at Tlantic, starting as a Project Manager before transitioning into the role of Principal Product Owner. Pedro is currently working as a Product Owner at Inovretail. Pedro has a strong background in Business Intelligence and data management, and has consistently delivered successful implementations and maintained strong relationships with clients throughout their career.
Pedro Couto Soares completed their undergraduate degree in Informatics and Computing Engineering at Faculdade de Engenharia da Universidade do Porto from 1999 to 2004. Prior to that, they attended Escola Secundária Garcia de Orta for their secondary education from 1992 to 1999. The education history does not provide any information about their earlier schooling at Escola da Vilarinha. Additionally, there is a mention of PMI, but no details about when they attended or what they studied there.
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Inovretail
InovRetail, a predictive analytics company focused on retail, working with players like Levi’s, Nike and others. 1. Predicting the sales baseline and the promotional impact for a Fashion retailer where they isolate what share of the sales are due to the Promotions in place, to the weather conditions, to a specific holiday and even to localfootball games – in some cases the difference between the predictions and the real observations are less than 2%!2. Predicting the churn patterns of another Fashion retailer’s clients, allowing them to predict who is more likely to churn and allowing them to take action before it is too late3. Predicting the products that are most likely to be out-of-stock and in what locations, optimizing the logistics and operations by several orders of magnitude – this is in place for Fashion, Food and now in Sports retailers.