Senior Data Scientist

Senior Data Scientist

- - - - - - - - - - - -

KEY EXPECTED ACHIEVEMENTS
Business understanding & Solution implementation
• The business need is understood and formalized in a descriptive datasheet or specifications
• The methods are identified through external researches and selected by their theoretical bases, advantages and drawbacks.
• The results of the analysis or the models are presented to the customers with data visualisation tools indicating the performances and the limits. The source code is delivered and explained if necessary.
Technical Capabilities

• Design, build, and create LLM-powered applications and agents for automation and workflow optimization.
• Develop reusable agentic components
• Work hands-on with Python, LangChain, and LlamaIndex.
• Leverage OpenAI APIs and open-source LLMs (e.g., GPT, LLaMA, Gemini, etc.).
• Prepare and process structured/unstructured datasets for fine-tuning or training models.
• Should have practical knowledge on traditional NLP algorithms/models (BERT/Transformer based models)
• Create scalable solutions on cloud platforms (Azure preferred) and manage the lifecycle with MLE best practices.
• Stay up to date with the latest GenAI/LLM advancements and apply them to real-world problems.
• Ability to design reusable, production-grade AI components.
• Practical experience with both proprietary (OpenAI) and open-source LLMs.
• Experience deploying solutions on Azure (preferred) or other major cloud platforms.
• Strong problem-solving skills with a focus on building, not theory.

Health of the metier
• The metier is well supported by the data scientist network with proposals for improvements to develop people, technos, processes.