Internship - Data Scientist Intern - Freight Rate Forecasting

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Job Description

Job Title: Internship - Data Scientist Intern - Freight Rate Forecasting

Req ID: 588748

Posting Start Date: 21/10/2026

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General information

Marseille, FRA

CMA CGM

Information Technology

Intern - Trainee

Full Time

Led by Rodolphe Saadé, the CMA CGM Group, a global leader in shipping and logistics, serves more than 420 ports around the world on five continents. With its subsidiary CEVA Logistics, a world leader in logistics, and its air freight division CMA CGM AIR CARGO, the CMA CGM Group is continually innovating to offer its customers a complete and increasingly efficient range of new shipping, land, air and logistics solutions.

Committed to the energy transition in shipping, and a pioneer in the use of alternative fuels, the CMA CGM Group has set a target to become Net Zero Carbon by 2050.

Through the CMA CGM Foundation, the Group acts in humanitarian crises that require an emergency response by mobilizing the Group’s shipping and logistics expertise to bring humanitarian supplies around the world.

Present in 160 countries through its network of more than 400 offices and 750 warehouses, the Group employs more than 155,000 people worldwide, including 4,000 in Marseilles where its head office is located.

YOUR ROLE

As part of your studies, you are currently looking for an end-of-studies internship (Bac+5 specializing in data science / data analysis).

You will join the Data Science team as part of the Lines’ digital transformation. Your main mission will be to build a freight rate forecasting model that meets business needs, and to help industrialize it. Your models’ results will directly help improve pricing strategies.

You may also work on a variety of topics: ad hoc data analyses, the Data Science department’s development workshops and the monitoring of existing projects. This internship will give you end-to-end experience, from scoping the need to deploying a model, on a subject at the crossroads of shipping, macroeconomics and geopolitics.

WHAT WILL YOU DO?

Scoping: You will help frame the project and write the specifications with business teams, then identify, analyze and collect the relevant data.

Modeling: You will design a modeling strategy and implement it, iterating until it meets the specifications. You will set up the evaluation and monitoring of candidate models.

Industrialization: You will write deployable code and produce a results report along with a roadmap for industrializing and deploying the model.

Business adoption: You will contribute to the model’s adoption plan, including its integration into pricing strategies.

Department support: You will carry out ad hoc data analyses, take part in development workshops and the monitoring of existing projects, and handle day-to-day requests from colleagues in the department.

WHO ARE WE LOOKING FOR?

Education: Bac+5 student specializing in data science / data analysis, ideally in your final year.

Technical skills:

Strong command of time series forecasting models

Proficiency in SQL and Python

Experience with Dataiku, Snowflake or GCP is a plus

Comfortable using AI to produce code in a controlled way

Languages: [to be completed – fluent English likely]

Personal qualities: Able to grasp complex and varied issues (how shipping works, macroeconomics, geopolitics, etc.); at ease working with many stakeholders and handling several topics in parallel.

JOB ENVIRONMENT

Location: Head Office – Marseille

Duration: 6 months, starting January 2027

Come along on CMA CGM’s adventure !

Information at a Glance

Rise in motion with CMA CGM Group: where ambition meets opportunity.

Join a global team of nearly 200 nationalities, shaping the future of global trade, moving essential goods, forging new paths, and pushing boundaries to serve an ever-changing world.

The pace is fast, the challenges are real, but the rewards are greater: growth, purpose, and the chance to make a meaningful impact. This is more than a job. It’s a journey on which you rise.

See how you can rise

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