Principal Data Scientist - Healthcare
Role details
Job location
Tech stack
Requirements
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.
Desirable
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). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Active contribution to open-source AI projects, research publications, and industry events/websites. Familiarity with AI security, privacy, and compliance standards (e.g. ISO 42001).