Data Scientist

Join our team as a Data Scientist II, where you'll work closely with Data Science, Product, and Engineering to build and improve ML and AI systems that drive operational value. This hands-on role focuses on text and language-based applications, and you'll contribute to the full lifecycle of production ML systems. Ideal candidates have experience in NLP and LLM-based systems, model deployment, and production ML workflows.

• Contribuer à l'ensemble du cycle de vie des systèmes ML de production, en mettant particulièrement l'accent sur les applications basées sur le texte et le langage.
• Développer, évaluer et itérer sur des systèmes basés sur le NLP et les LLM, y compris la classification de texte, l'extraction d'informations et les pipelines de récupération de contexte.
• Collaborer avec des ingénieurs pour soutenir le déploiement, l'intégration et la surveillance des systèmes ML et AI en production.
• The ideal candidate is comfortable operating in ambiguous problem spaces, can translate loosely defined business needs into concrete technical approaches, and communicates findings clearly to both technical and non-technical audiences
• Experience with model deployment, monitoring, or production ML workflows is a plus
• Experience with both prompt engineering and fine-tuning approaches for language tasks, with the judgment to know when to apply each
• Transportation or logistics industry experience is a plus
• Experience with Hugging Face Transformers for text classification or related NLP tasks
• Familiarity with text classification, information extraction, or other NLP tasks — and an understanding of where these systems fail
• Experience designing data annotation workflows, labeling guidelines, or label quality processes is a plus
• Proficiency in Python and SQL, and comfort working with structured and unstructured data
• Familiarity with LangChain and LangSmith or similar LLM orchestration and observability tooling is a plus
• Strong written communication skills; able to document systems and findings clearly and present recommendations to non-technical stakeholders
• Hands-on experience building or improving NLP or LLM-based systems in applied settings
• Familiarity with modern retrieval strategies and RAG architectures and how they affect LLM system performance
• Bachelor's or Master's degree in a quantitative field (computer science, statistics, linguistics, or related) and 2–4 years of applied ML or data science experience, or equivalent practical experience
• Experience contributing to evaluation frameworks, test sets, or performance diagnostics for ML systems, including comfort with statistical methods for measuring model performance
• Ability to operate effectively in ambiguous problem spaces — scoping technical approaches when requirements are not fully defined