INTERNSHIP DETAILS

Stage - Data Analyst

CompanyCMA CGM
LocationCourbevoie
Work ModeOn Site
PostedFebruary 23, 2026
Internship Information
Core Responsibilities
The intern will contribute to analyzing data quality and evaluating the robustness of predictive models used in OneCockpit by exploiting Databricks environments and CEVA's SQL databases. Key tasks involve extracting, manipulating, and analyzing data, writing and optimizing SQL queries for data quality control, and testing performance indicators for predictive models.
Internship Type
full time
Company Size
30910
Visa Sponsorship
No
Language
French
Working Hours
40 hours
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About The Company
The CMA CGM Group is a global player in sea, land, air and logistics solutions, true to its corporate Purpose, "We imagine better ways to serve a world in motion". Present in 177 countries, it employs 160,000 people, of which nearly 6,000 in Marseilles where its head office is located. The world's 3rd largest shipping company, CMA CGM serves more than 420 ports across 5 continents with a fleet of over 650 vessels. In 2024, CMA CGM carried over 23 million TEU (twenty-foot equivalent unit) containers. Its subsidiary CEVA Logistics, one of the world's top five players, operates 1,000 warehouses and handled 15 million shipments in 2024. CMA CGM AIR CARGO, the Group's air freight division, will operate a fleet of 6 cargo aircraft by 2025. CMA Media, France's 3rd largest private media group, includes RMC-BFM and several national and regional press titles (La Tribune Dimanche, La Tribune, La Provence and Corse Matin). Committed to energy transition, the CMA CGM Group is aiming for Net Zero Carbon by 2050. The CMA CGM Foundation provides humanitarian aid in crisis situations, and is committed to education for all and equal opportunities throughout the world. To date, the CMA CGM Foundation has transported 63,000 tons of humanitarian aid to 97 countries and supported over 550 educational projects.
About the Role

CEVA Logistics provides global supply chain solutions to connect people, products, and providers all around the world. Present in 170+ countries and with more than 110,000 employees spread over 1,500 sites, we are proud to be a Top 5 global 3PL.

We believe that our employees are the key to our success. We want to engage and empower our diverse, global team to co-create value with our customers through our solutions in contract logistics and air, ocean, ground, and finished vehicle transport. That is why CEVA Logistics offers a dynamic and exceptional work environment that fosters personal growth, innovation, and continuous improvement.

DARE TO GROW! Join CEVA Logistics, and you will be part of a team that values imagination and continued learning and is committed to excellence in everything we do. Join us in our mission to shape the future of global logistics. As we continue growing at a fast pace, will you “Dare to Grow” with us?

 

LES MISSIONS :

Dans le cadre de l'accompagnement de la stratégie Achat/opérations, nous recherchons un(e) stagiaire afin de contribuer à l’analyse de la qualité des données et à l’évaluation de la robustesse des modèles prédictifs utilisés dans OneCockpit, en exploitant les environnements Databricks et les bases SQL de CEVA.

Intégré(e) au sein de l’équipe Data Air & Ocean, vous serez rapidement responsabilisé(e) sur vos sujets et serez amené à mener des analyses de Data approfondies dans le domaine du transport.

Votre mission s’articulera ainsi autour de trois axes :

PROJET

  • Extraire, manipuler et analyser des données issues de OneCockpit et de Databricks.
  • Écrire et optimiser des requêtes SQL pour :
    • Contrôler la qualité des données (complétude, cohérence, duplications, anomalies),
    • Tester des indicateurs de performance des modèles prédictifs.
  • Participer au monitoring de la data quality pipeline et aider à identifier les sources d’erreurs.
  • Réaliser des analyses exploratoires (profiling de datasets, détection d’outliers, tendances…).
  • Documenter les résultats, recommandations et anomalies détectées.
  • Collaborer avec l’équipe Data Management & Innovation pour mettre en place des actions correctives.
  • Contribuer à l’amélioration des processus d’ingestion et de validation des données.

SUPPORT

  • Support aux utilisateurs & documentations
  • Suivi de bugs & résolution non technique

PROCESSUS INTERNES

  • Mise à jour des documents de référence
  • Mise en place de KPI de mesure de la performance interne

LE PROFIL :

De formation supérieure (Bac+5), type ingénieur / école de commerce / logistique, vous avez des connaissances en gestion de projet.

Organisé(e), rigoureux(se) et autonome, vous avez de fortes capacités d’adaptation et êtes force de proposition.

Bases en SQL (jointures, agrégations, CTE, analyses…), Connaissance des environnements cloud data : Databricks, (un plus, mais pas obligatoire).

Bonne compréhension des concepts de data quality : règles de gestion, contrôles, validation.

Notions en statistiques pour l’analyse de modèles de prédiction (MAE, RMSE, accuracy…).

Capacité à visualiser des résultats (Power BI, Python ou Excel – bonus mais optionnel).

As a global organization, and as part of the CMA CGM Group, diversity is critical to our business success; only when we can reflect the cultures, languages, behaviors and local knowledge of our customers, we can succeed. By employing people with different experiences and abilities, we expand our knowledge and increase our creativity and innovation. 

Please note:  Legitimate CEVA Logistics recruitment processes include communication with candidates through recognized professional networks, such as LinkedIn or via an official company email address: firstname.lastname@cevalogistics.com. We recommend that you do not respond to unsolicited business propositions and/or offers from people with whom you are unfamiliar.

Key Skills
SQLData AnalysisData QualityDatabricksData ManipulationPredictive ModelsData ProfilingOutlier DetectionData IngestionData ValidationProject ManagementStatisticsPower BIPythonExcel
Categories
Data & AnalyticsLogisticsTransportationTechnology