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Senior Data Scientist - Demand Forecast

Decathlon Digital EN·EU·Paris, Paris, France·senior
mldata science
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About Decathlon Decathlon aims to become the best sports digital platform and open ecosystem in the world. We want to enable customers to experience Decathlon through many local sport-centric experiences by connecting many third-party actors and services, in a secure and performant way. Our digital teams in Lille, Paris, and Amsterdam (and more…) which bring together more than 5000 collaborators are united to build and scale digital products with the aim to always deliver the best value to our users. With a presence in more than 70 countries, Decathlon is committed to innovation, sustainability, and customer satisfaction. Job Description Decathlon is seeking a talented and experienced Data Scientist to join our Demand and Assortment Planning team within the Supply Chain department. As a Senior Data Scientist, you will play a critical role in analyzing data, developing AI models to production, and providing insights to optimize demand forecasting and assortment planning processes. We welcome candidates with expertise in time series forecasting and a passion for leveraging data to drive business decisions. Responsibilities Analyze historical sales data and market trends to forecast demand for Decathlon products. Develop and implement advanced statistical models and machine learning algorithms for demand forecasting and assortment planning, including foundation model. Collaborate with cross-functional teams including Supply Chain Business, Engineering, and Product Development to align Data and AI efforts with business objectives. Evaluate and improve existing forecasting models and methodologies to enhance accuracy and efficiency. Communicate insights and recommendations to stakeholders through clear and concise data visualizations and presentations. Stay updated on industry trends and emerging technologies in data science and supply chain management. Hard Skills Proficiency in statistical analysis,machine learning techniques, time series time series analysis (stationa

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