Data Scientist

Inetum is a European leader in digital services. Inetum’s team of 28,000 consultants and specialists strive every day to make a digital impact for businesses, public sector entities and society. Inetum’s solutions aim at contributing to its clients’ performance and innovation as well as the common good.

Present in 19 countries with a dense network of sites, Inetum partners with major software publishers to meet the challenges of digital transformation with proximity and flexibility.

Driven by its ambition for growth and scale, Inetum generated sales of 2.5 billion euros in 2023.

Job Description

• Design & Deploy: Create machine learning models and tailored AI solutions.
• Innovate: Fine-tune and extend Large Language Models (LLMs) including open-source and commercial models.
• Optimize: Implement Retrieval-Augmented Generation (RAG) techniques for enhanced model efficiency.
• Analyze: Perform exploratory data analysis and feature engineering to unlock better model performance.
• Build Generative AI: Develop cutting-edge models for content creation, process automation, and innovation.
• Apply Traditional ML: Utilize decision trees, SVMs, neural networks, and more to solve diverse problems.
• Collaborate: Work alongside data engineers, product managers, and stakeholders.
• Quality Assurance: Ensure high-quality AI solutions through rigorous testing and validation.
• Document & Share: Maintain technical documentation for knowledge sharing and reproducibility.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
• 3+ years of hands-on experience in machine learning and AI development.
• Strong expertise in Generative AI (model training, fine-tuning, and deployment).
• Experience with open-source and commercial LLMs.
• Proficiency in implementing RAG techniques.
• Experience with vector databases.
• Advanced programming skills in Python with frameworks like TensorFlow, PyTorch, and Hugging Face.
• Deep understanding of NLP techniques and architectures.
• Practical experience with traditional AI/ML methods such as decision trees, SVMs, and neural networks. Practical experience with traditional AI/ML methods such as decision trees, SVMs, clustering and neural networks.
• Familiarity with cloud platforms (AWS, GCP, or Azure) for AI/ML deployments.
• Knowledge of MLOps practices and tools.
• Strong problem-solving and analytical skills.
• Effective communication in public and teamwork skills.
• Fluency in English
• Willingness to learn French.

Nice-to-Have Skills:

• Knowledge of reinforcement learning from human feedback (RLHF).
• Familiarity with prompt engineering.
• Understanding of ethical AI principles and model interpretability.
• Fluency in French is a plus.