Tech Lead Data Scientist - Downstream Demand Forecast

About Our Company Digital

Our company's ambition is to become the leading digital global sports platform and a major open ecosystem. We aim to enable users to experience our brand through numerous localized sport-centric experiences by connecting multiple third-party partners and services in a secure and high-performing way. Our Digital division today represents more than 5,000 technical profiles—software engineers, product managers, data experts, Cloud, and cybersecurity specialists—operating across several major European hubs.

Our Downstream Demand Forecast team is looking for a new Tech Lead Data Scientist.

The Downstream Demand Forecast team, part of the Data Supply Chain division, is responsible for the design, development, and maintenance of innovative analytical and Artificial Intelligence solutions tailored to supply chain use cases such as multi-echelon demand forecasting, target stock estimation, or shortage management, to name a few. You will work within a multidisciplinary squad composed of Data Scientists, Data Analysts, Analytics Engineers, and ML Engineers, operating in an agile manner and in close collaboration with our Product and Engineering partners as well as our global business end-users.

Objectifs et livrables

Your Mission: Lead, Architect, and Scale the Future of Demand Forecasting

As a Tech Lead Data Scientist & ML Engineer, you will be the technical backbone and strategic engine of our Downstream Demand Forecasting team. You will play a pivotal role in bridging the gap between high-level business strategy and robust technical execution.

Your mission is to lead the design and deployment of production-grade ML models on demand forecasting for various channels and granularities, ultimately optimizing stock management across our entire supply chain. You aren't just building models; you are mentoring a team of more than 5 data experts, ensuring technical excellence, and acting as the primary technical partner for the Product Manager and the Data Manager to turn business requirements into scalable reality.

Your Responsibilities

Technical Leadership & Strategy

• Architectural Ownership: Define the technical roadmap for demand forecasting, ensuring that ML models are not only accurate but built for industrial-scale production on large datasets.

• PM Partnership: Act as the primary technical stakeholder for the Product Manager, translating business needs into technical specifications and managing the feasibility of the product roadmap.

• Innovation Oversight: Stay ahead of industry trends in demand forecasting, evaluating and integrating emerging technologies to maintain a competitive edge.

Team Coordination & Mentorship

• Technical Stewardship: Coordinate the daily technical initiatives of a >5-person data team, ensuring alignment, removing blockers, and maintaining high velocity.

• Quality Assurance: Lead the code and model review process, establishing best practices for ML engineering, reproducibility, and documentation to upskill junior profiles.

• Talent Development: Foster a culture of technical excellence, guiding team members through complex problem-solving and professional growth.

Advanced ML Engineering

• High-Impact Modeling: Lead the development of advanced statistical and ML algorithms that integrate internal variables (price, assortment) with external signals (weather, calendar data, market trends) for global replenishment.

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

• Stakeholder Communication: Translate complex data insights into actionable strategic recommendations for Supply Chain and Business leadership through compelling visualization and storytelling.

Who You Are

• You have deep mastery of Time Series forecasting and have successfully moved multiple ML models from "notebook" to "production" at scale.

• You have experience in leading technical teams, with a proven ability to review work critically yet constructively.

• You understand supply chain concepts, particularly stock optimization and demand forecasting. You know how to transform business needs into well-defined problems and concrete (actionable) recommendations.

• You can clearly explain model’s predictions to non-technical stakeholders just as easily as you can discuss gradient boosting hyperparameters with an engineer.

• You balance the desire for state-of-the-art research with the business necessity of delivering reliable, maintainable code.

• You have a professional command of English.

Technical stack

The technical environment:

• Execution Engine: Databricks, AWS, GCP

• Payload: Python, dbt, Scikit-Learn, Pytorch, Pyspark

• CI/CD: Github Actions

• Serving: Docker, Protobuf, gRPC

• Model registry, Model Tracking: MLFlow

• Orchestration: Airflow

• Documentation / code: Git, Confluence

• Data Visualisation: Tableau