Principal Data Scientist - Healthcare

- Proven track record of accountability for the delivery of complex, production-grade AI/ML solutions at scale. - Demonstrable experience of technical leadership in AI delivery. - Deep expertise in developing and assuring advanced AI/ML models, including time series, supervised/unsupervised learning, reinforcement learning, LLMs and agentic AI. - Experience with the latest AI engineering approaches such as prompt engineering, retrieval-augmented generation (RAG) and orchestration of agentic AI systems. - Expertise in data engineering for AI: handling large-scale, unstructured, and multimodal data, and integrating non-traditional data sources. - Deep understanding of responsible AI principles, model interpretability and ethical considerations, with a track record of influencing policy and standards. - Ability to communicate and negotiate with C-level and senior stakeholders, translating complex technical concepts into business value. - Experience in developing and executing account strategies, shaping commercial AI offerings and driving business development in partnership with sales and account managers. - Demonstrated ability to build and lead high-performing teams and wider AI and data science communities. - Strong commercial acumen with a history of influencing the commercial success of AI products and solutions. - Experience with modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), and advanced ML libraries (e.g. scikit-learn, XGBoost) (desirable). - Experience with data storage for AI, vector databases, semantic search, and knowledge graphs (desirable). - Active contribution to open-source AI projects, research publications, and industry events/websites (desirable). - Familiarity with AI security, privacy, and compliance standards (e.g. ISO 42001) (desirable).