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 looking for a 4- to 6-month internship to complete your degree (Master's 2, engineering school or equivalent).

You will join a Data Science team working on an applied research project: RAFA, an artificial intelligence agent that automates part of the data scientist's experimental cycle. RAFA designs, codes, runs, repairs and analyses machine learning experiments, with a particular focus on multi-horizon time series forecasting applied to shipping lines.

Your role will be to make RAFA a more reliable, robust and useful tool: improving the agent's experimental logic, validating results, preventing data leakage and integrating advanced Agentic AI and Deep Learning models.

WHAT WILL YOU DO?

• Experimentation logic : You will improve how model families are selected and structured, the parent/child logic of the experiment tree and the reuse of previous results, so that RAFA progressively learns which experiments are relevant and can fall back on a robust baseline.
• Forecasting & backtesting : You will work on multi-horizon forecasting (time-based supervised datasets, lags, lagged features, per-horizon metrics) and rigorous backtesting, with one key goal: beating a strong baseline through residual models, baseline/model gating or cautious baseline correction.
• Agentic AI & multi-agent orchestration : You will help orchestrate RAFA's specialised agents (analyst, planner, coder, repairer, reviewer, validator, reporter): robust prompts, experiment memory, automated validation, anti-leakage guardrails and a reliable execution graph.
• Applied Deep Learning : You will integrate and evaluate neural models (MLP with embeddings, TCN, RNN/LSTM/GRU, temporal Transformers, hybrid baseline + correction models) to identify where deep learning brings real gains over classical models.
• Code generation & repair : You will make the conversion of an experiment plan into executable Python code more reliable: better coder prompts, static checks, detection of temporal leakage, analysis of failure causes and fewer unnecessary repair loops.
• Reporting & documentation : You will produce HTML reports, experiment-tree visualisations, technical documentation of the agent and summaries for data scientists and management.

WHO ARE WE LOOKING FOR?

• Education : You are a Master's 2 or engineering school student (or equivalent), specialising in data science, machine learning, artificial intelligence, statistics or time series.
• Technical skills : You are proficient in Python (pandas, NumPy, scikit-learn), Jupyter and Git. You have solid foundations in supervised machine learning, forecasting, time-based backtesting and regression metrics (WAPE, MAPE, MAE, RMSE), and a genuine awareness of data leakage issues.
• Agentic AI : You have initial experience with LLM agents and orchestration frameworks (LangGraph, LangChain or equivalent), prompt engineering and automated code generation.
• Deep Learning : You are comfortable with PyTorch and time series architectures (MLP, TCN, RNN/LSTM/GRU, Transformers), as well as training techniques (regularisation, early stopping, loss optimisation).
• Nice to have : Dataiku, FastAPI, Docker, MLflow, OpenAI API or equivalent, NetworkX/Graphviz, LLM observability.
• Languages : Professional English required.
• Personal qualities : You are curious, rigorous and autonomous, with a strong interest in AI agents and exploratory topics. You can analyse experimental results and document your work clearly.

JOB ENVIRONMENT

• Location : MARSEILLE
• Internship : 6 months from January

Come along on CMA CGM’s adventure !