Principal Data Scientist – Healthcare

Overview

Principal Data Scientist at Kainos responsible for the successful delivery of large-scale, high-impact AI solutions. Drive the direction of AI and data science across the business, promoting modern AI development practices and scalable cloud-native architectures at enterprise scale. Provide technical and thought leadership, engaging with C-level and senior stakeholders to define architectural principles and strategic direction. Foster a culture of innovation, continuous learning, and engineering excellence, and lead a community of data scientists, AI engineers, and technical managers to adopt robust standards and responsible AI practices.

Responsibilities

• Lead the delivery of complex, production-grade AI/ML solutions at scale, ensuring measurable business value for customers.
• Set the direction for AI and data science initiatives across the business and promote modern AI development practices.
• Provide technical and thought leadership, influencing architectural principles and strategic direction with C-level and senior stakeholders.
• Foster a culture of innovation, learning, and engineering excellence within Kainos and the wider industry.
• Mentor and develop a community of data scientists, AI engineers, and technical managers; ensure adoption of robust standards and responsible AI practices.
• Build enduring customer relationships and develop alliances with technology partners; shape Kainos’ commercial AI offerings.
• Embed commercial acumen to influence account strategies and ensure customers derive business value from AI investments.
• Communicate and negotiate complex technical concepts with executive stakeholders, translating them into business value.
• Collaborate with sales and account managers to develop and execute account strategies and drive business development in AI.

Qualifications

• Proven track record of accountability for the delivery of complex, production-grade AI/ML solutions at scale.
• Demonstrated technical leadership in AI delivery and deep expertise in developing and ensuring high-quality AI/ML models, including time series, supervised/unsupervised learning, reinforcement learning, LLMs, and agentic AI.
• Experience with modern 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; integrating non-traditional data sources.
• Solid understanding of responsible AI principles, model interpretability, and ethical considerations, with a track record of influencing policy and standards.
• Ability to communicate with and influence C-level and senior stakeholders, translating technical concepts into business value.
• Experience in developing and executing account strategies, shaping commercial AI offerings, and collaborating with sales and account teams.
• Proven ability to build and lead high-performing teams and wider AI/data science communities; strong commercial acumen.

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).

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