Machine Learning Engineer - PRISM - Supply Chain (f/m/d)

Publiée le 05/08/2026

Mission

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 Senior Machine Learning Engineer (MLE) to join our Demand and Assortment Planning department within the Supply Chain division. As an MLE at Decathlon, you will play a critical role in the industrialization, deployment, and monitoring of AI models. You will collaborate closely with Data Scientists, Data Engineers and Digital Product Managers to design and implement cutting-edge AI solutions that optimize our supply chain operations.

Responsibilities

• Develop and implement AI models for demand and assortment planning in the supply chain domain.
• Collaborate with Data Scientists and Digital Product Managers to translate business requirements into scalable machine learning solutions.
• Collaborate with data engineering teams to optimize data pipelines and ensure timely availability of clean and relevant data for model training and inference.
• Implement strategies for model retraining and updating to adapt to changing market dynamics and business requirements.
• Industrialize machine learning models and deploy them into production environments.
• Monitor model performance and implement improvements to ensure accuracy and reliability.
• Work closely with cross-functional teams to integrate machine learning capabilities into existing systems and processes.
• Document best practices, standard operating procedures (SOPs), and technical specifications for machine learning models and deployment processes.
• Provide mentorship and guidance to junior members of the team, fostering a culture of continuous learning and knowledge sharing.
• Stay updated on emerging trends and best practices in machine learning and supply chain optimization.

Hard Skills

• Proficiency in machine learning techniques, particularly Time Series Forecasting.
• Strong programming skills in Python.
• Experience with machine learning libraries/frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Familiarity with data engineering concepts and tools for data preprocessing, feature engineering, and model evaluation.
• Familiarity with industrializing and deploying machine learning models in production environments (MLOps, Containerization, orchestration, CI/CD, Infrastructure as Code, Monitoring and Logging, Scalability and Performance Optimization, Interfacing, parallelization, gpu computing, automatic backtesting...).
• Knowledge of supply chain processes and dynamics is a plus.

Soft Skills

• Excellent communication and collaboration skills.
• Ability to work effectively in a fast-paced, dynamic environment.
• Strong problem-solving skills and attention to detail.
• Adaptability and willingness to learn new technologies and methodologies.
• Ability to translate complex technical concepts into understandable terms for non-technical stakeholders.
• Ability to understand users challenges and associated needs

Qualifications

Basic Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field.
• 5 years of experience in machine learning engineering or a similar role.
• Demonstrated experience in developing and deploying machine learning models.

Preferred Qualifications

• Master's degree or higher in a relevant field.
• Experience in the supply chain domain, particularly in demand and assortment planning.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Experience with containerization technologies such as Docker and Kubernetes.
• Knowledge of Big Data technologies such as Spark and Kafka.
• Previous experience with Agile methodologies and DevOps practices.

Technical Environment

• Execution Engine: Databricks, AWS EKS, Sagemaker
• Payload: Python, Spark, Scikit-Learn, Tensorflow / Pytorch, Pyspark
• CICD: Github Actions
• Serving: Docker, Protobuf, g