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Supply Chain Forecasting

Retail – Supply Chain Forecasting & R&D

Demand forecasting (LSTM, probabilistic models) and MLOps industrialization on Databricks.

Multi-scale forecasting

From product to stock, from day to season, with probabilistic forecasts

Project Details

Client

Decathlon

Sector

Retail – Supply Chain Forecasting & R&D

Year

2023

Key Technologies

TensorFlowPySparkDatabricksAirflow

Mission

Lead Senior Data Scientist - Supply Chain Forecasting

Supply Chain Forecasting & Optimization

Long-term assignment, Paris, France.

Demand forecasting: Development of predictive models, including RNN and LSTM networks to capture complex temporal demand dynamics. Inventory & replenishment optimization: Reduction of stockouts and overstocks. Industrialization: Pipeline automation, continuous integration, model monitoring.

Contexte & Environnement

Sector: Mass retail, e-commerce, omnichannel retail. Supply chain complexity: multi-warehouse and multi-channel. Dynamic consumer behavior and strong seasonality. Tight logistics constraints (lead times, capacity, cost). Project team: 1 PManager / 4 Data Scientists / 2 Data Analysts / 1 ML Engineer.

Team

1 Project Manager / 4 Data Scientists / 2 Data Analysts / 1 ML Engineer

Environment

Paris, France – Databricks, AWS S3, Airflow, Delta Lake, MLflow