Senior Data Scientist

Contexte de la mission

A leading global sports retailer is accelerating its transformation into a premier digital sports platform, aiming to connect third-party players and services through a secure, open ecosystem. To support this vision, the company is looking for a Senior Data Scientist to join its retail data team, which is dedicated to revolutionizing in-store activities.

In this role, you will provide valuable insights to digital teams, optimize the in-store tool ecosystem, and empower internal users to drive stronger business impact and operational performance using innovative data solutions. This internal, permanent position can be based in major French tech hubs such as Paris, Lille, or Nantes.

Objectifs et livrables

Key Responsibilities

Computer Vision:

• Architect, develop, and deploy computer vision models tailored for processing visual data across both real-time and offline systems.

• Leverage advanced methodologies like image classification, object tracking, and semantic segmentation to unlock valuable insights from visual assets.

• Partner with multidisciplinary product groups to integrate visual intelligence models into live business workflows, personalization engines, and digital applications.

• Perform critical technical evaluations of external vendor software, establishing robust validation frameworks and benchmarks to verify supplier performance.

• Maintain a deep understanding of current trends in deep learning and spatial computing to consistently introduce cutting-edge techniques to the team.

Collaboration & Communication:

• Align with engineering, product, and business units to scope technical data prerequisites and provide impactful analytics.

• Translate highly complex analytical concepts and model architectures into clear, business-driven narratives for diverse audiences.

• Standardize documentation of workflows, codebases, and experiments to guarantee long-term model maintainability and reproducibility.

• Offer data-driven guidance on vendor selections to steer optimal "build-versus-buy" architectural and strategic paths.

About the Digital Division

What if technology allowed us to reshape the future of physical activities and establish the world's premier digital sports platform? This is the core ambition driving our international sports retail organization.

Our foundational purpose has always been to make physical activity and its benefits widely available to a global community over the long term. Today, this mission is amplified through state-of-the-art technological experiences designed to cater to 500 million active users globally.

To achieve this, the company is building an array of modern offerings for athletes, ranging from digital coaching and custom loyalty initiatives to connected retail systems, recommerce (second-hand goods), and equipment rental programs from internal and external brands.

Our global tech ecosystem brings together a community of over 5,000 specialists, encompassing software developers, data practitioners, cloud architects, product leaders, and cybersecurity professionals across several prominent European technology hubs.

We are deeply committed to fostering an inclusive work environment free of discrimination, championing gender equity, age diversity, and accessibility. We look for unique personalities and diverse viewpoints, believing that a varied team is the ultimate catalyst for creativity and organizational success.

Skills

Experience:

• At least 3 to 7+ years of industry experience engineering machine learning systems, with a dedicated specialization in computer vision and visual recognition.

• Proven track record of cross-functional communication, comfortably bridging technical engineering and non-technical business domains.

• Academic credentials or heavy experience in a quantitative discipline (e.g., Data Science, Applied Mathematics, or Computer Science).

• Full professional proficiency in both written and spoken English and French.

Technical Skills:

• High proficiency in Python coupled with extensive knowledge of core statistical, data science, and modeling frameworks.

• Practical, hands-on mastery of object detection pipelines, segmentation workflows, and feature engineering approaches.

• Good understanding of production-level MLOps paradigms, including container management, workflow orchestration, CI/CD pipelines, and automated testing frameworks. Capable of driving logging, performance tuning, and scaling operations for model monitoring.

Nice to have:

• Background in multi-modal architectures (combining imagery with telemetry, textual data, or voice inputs).

• Understanding of deployment strategies on CPU constraints or edge computing devices.

• Combined experience launching zero-to-one greenfield products and operating within sophisticated, matrixed enterprise engineering systems.

Technical Environment

• Execution Engine: Databricks, AWS EKS, Sagemaker

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

• CI/CD: Github Actions

• Serving: Docker, Protobuf, gRPC

• Model registry, Model Tracking: MLFlow

• Orchestration: Airflow

• Documentation / code: Git, Confluence

• Data Visualisation: Tableau