🔬 DATA SCIENTIST (M/F)

Data scientists solve business problems using statistical modeling and machine learning: prediction, scoring, recommendations, anomaly detection, and experimentation. By 2026, the field will have widely integrated large language models (LLMs) and generative AI into its toolkit.

🎯 Tasks

• Framing a business problem as a data problem (and knowing when ML isn't the solution).
• Explore, prepare, and analyze data; design features.
• Train, evaluate, and compare models (traditional machine learning, deep learning, LLM).
• Design rigorous experiments (A/B tests, causality).
• Collaborate with ML engineers and data engineers on the deployment to production.

🛠️ Skills & Tech Stack 2026

• Python: pandas, scikit-learn, PyTorch, statsmodels.
• SQL and modern data warehouses (Snowflake, BigQuery, Databricks).
• GenAI: LLM APIs (OpenAI, Anthropic, Mistral), RAG, model evaluation.
• Solid Statistics: Inference, Causality, Experimental Design.
• MLOps Concepts: Versioning, Reproducibility, and Drift Monitoring.

💰 2026 Salaries (as reported in France)

• Junior: 42–50 k€
• Confirmed: 50–70 k€
• Senior / Lead: 70–100 k€ and up (even higher in cutting-edge AI)
• Freelance: Average daily rate of 550–900 €.

🔍 Not to be confused with

The Data Analyst (descriptive analysis and BI), the ML Engineer (model deployment), andthe AI Engineer (applications built on LLMs).

❓ Frequently Asked Questions

Should a data scientist know how to put systems into production? Increasingly, yes: “full-stack data science” professionals—who are capable of handling the entire process through to deployment—are the most highly valued.

Is a PhD required? No, except in applied research; practical experience and experimental rigor are more important in the corporate world.

📈 Trends

Senior/Lead Data Scientist, ML Engineer, AI Engineer, Head of Data Science.