Data Scientist - Supply Chain Forecasting

Context

The team, part of the Data Supply Chain, is responsible for the design, development, and maintenance of innovative analytical and Artificial Intelligence solutions around supply chain use cases such as multi-echelon demand forecasting, target stock estimation, or shortage management. The team is multidisciplinary, composed of Data Scientists, Data Analysts, Analytics Engineers, and ML Engineers, working in an agile manner and in close proximity with Product and Engineering contacts as well as business users.

Missions

Analyse, develop and deploy Downstream Demand Forecasting solutions at scale

• Develop and deploy production-grade ML models for demand forecasting across store and Ecommerce channels on different geographical granularities (stores, warehouses), directly contributing to stock management optimization.

• Build robust models collaborating with a multidisciplinary team of data experts.

• Partner with Product and Business stakeholders to translate business requirements into scalable, reliable solutions.

Advanced Data Science

• Conduct in-depth diagnostic analyses of production forecasts and user-driven overrides.

• Identify patterns, refine model accuracy, accelerate deployment cycles, and drive automation initiatives.

• Develop advanced statistical and ML algorithms integrating internal variables (price, assortment) with external signals (weather, calendar data, market trends) for global replenishment.

• Manage the end-to-end lifecycle of ML solutions—from R&D to CI/CD deployment—ensuring stability and efficiency of the forecasting engine.

• Translate complex data insights into actionable recommendations for Supply Chain and Business stakeholders through compelling visualization and storytelling.

Team Collaboration

• Participate actively in daily technical initiatives, contributing to codebases, removing blockers, and maintaining high velocity within the team.

• Participate in code and model review processes, promoting best practices for ML engineering, reproducibility, and documentation.

• Collaborate with team members to foster a culture of technical excellence and knowledge sharing.

Tools & Environment

• Execution Engine: Databricks, AWS, GCP

• Payload: Python, Spark, Scikit-Learn, Tensorflow / Pytorch, Pyspark

• CI/CD: Github Actions

• Serving: Docker, Protobuf, gRPC

• Model registry, Model Tracking: MLFlow

• Orchestration: Airflow

• Documentation / code: Git, Confluence

• Data Visualization: Tableau

Working Conditions

• Location: Lille

• Daily rate: 600€

1. Good knowledge of Time Series forecasting with successful experience working on ML pipelines in production at scale
2. Experience working in collaborative technical teams with strong ability to give and receive constructive feedback
3. Understanding of supply chain concepts, particularly stock optimization and demand forecasting
4. Ability to transform business needs into well-defined technical problems
5. Ability to clearly explain model predictions to non-technical stakeholders and discuss gradient boosting hyperparameters with engineers
6. Balance between state-of-the-art research and delivering reliable, maintainable code
7. Professional command of English
8. Expertise in Machine Learning and Python programming
9. Advanced skills in Databricks and dbt
10. Confirmed skills in Time Series forecasting and Supply Chain