Lead Data Scientist - Healthcare

About the Role

The AI and Data Practice at Kainos brings together deep expertise in machine learning, generative AI, and data engineering. As a pioneer in responsible AI, Kainos has authored the UK government's AI Cyber Security Code of Practice implementation guide and partners with leading organizations to ensure AI is deployed ethically, securely, and with measurable business value.

Your day-to-day responsibilities will include architecting, designing, and delivering advanced AI solutions, as well as driving the adoption of modern AI frameworks and AIOps best practices. You will work closely with clients to understand their business challenges and develop tailored AI solutions that meet their needs.

What You Will Do

• Architect, design, and deliver advanced AI solutions leveraging state-of-the-art machine learning, generative, and agentic AI technologies
• Drive the adoption of modern AI frameworks, AIOps best practices, and scalable cloud-native architectures
• Collaborate with customers to translate business challenges into trustworthy AI solutions
• Ensure responsible AI practices throughout the development and deployment process
• Foster a culture of innovation, continuous learning, and engineering excellence within the team
• Manage, coach, and develop a small team of data scientists and engineers
• Provide direction and leadership for the team to solve challenging problems together
• Stay up-to-date with new technologies and approaches, applying this knowledge to improve AI solutions
• Develop and maintain relationships with key clients and stakeholders
• Contribute to the development of Kainos' AI and data strategy

What We Are Looking For

• A minimum of a 2.1 degree in Computer Science, AI, Data Science, Statistics, or a similar quantitative field
• Deep understanding and development of AI/ML models, including time series, supervised/unsupervised learning, reinforcement learning, and LLMs
• Experience with the latest AI engineering approaches, such as prompt engineering, retrieval-augmented generation (RAG), and agentic AI
• Strong Python skills with a grounding in software engineering best practices (CI/CD, testing, code reviews, etc.)
• Expertise in data engineering for AI, handling large-scale, unstructured, and multimodal data
• Understanding of responsible AI principles, model interpretability, and ethical considerations
• Strong interpersonal skills, with the ability to lead client projects and establish requirements in non-technical language
• Experience in managing, coaching, and developing junior members of a team and wider community

Nice to Have

• Demonstrable experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow)
• Fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini)
• Experience with machine learning libraries (e.g., scikit-learn, XGBoost)
• Knowledge of data storage for AI, vector databases, and semantic search

Benefits and Perks

• Opportunity to work on cutting-edge AI projects with a pioneering company
• Collaborative, people-first culture with a focus on innovation and growth
• Professional development and coaching to help you achieve your career goals
• Access to the latest AI tools and technologies
• Flexible working arrangements, including remote work options
• Competitive compensation package
• Recognition and reward for outstanding performance
• Opportunity to work with a diverse range of clients and stakeholders

• Tip: Develop a strong understanding of responsible AI principles and model interpretability to stand out in the application process.
• Showcase your experience with modern deep learning frameworks and AI engineering approaches in your portfolio or resume.
• Prepare to discuss your approach to data engineering for AI and handling large-scale, unstructured, and multimodal data during the interview.
• Highlight your ability to communicate complex AI concepts to non-technical stakeholders, demonstrating strong interpersonal skills.
• Research Kainos' commitment to innovation and responsible AI practices, and be prepared to discuss how you can contribute to these efforts.
• Be prepared to negotiate your salary based on your experience and qualifications, and don't be afraid to ask about benefits and perks.