Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Lead Data Scientist - Healthcare

Kainos
City of London
3 weeks ago
Create job alert

Join to apply for the Lead Data Scientist - Healthcare role at Kainos.


Join Kainos and shape the future. At Kainos, we’re problem solvers, innovators and collaborators – driven by a shared mission to create real impact. Whether we’re transforming digital services for millions, delivering cutting‑edge Workday solutions or pushing the boundaries of technology, we do it together.


Main Purpose of the Role & Responsibilities

As a Lead Data Scientist, you will architect, design and deliver advanced AI solutions using state‑of‑the‑art machine learning, generative and agentic AI technologies. You’ll champion modern AI frameworks, AIOps best practices and scalable cloud‑native architectures. The role involves hands‑on technical leadership and collaboration with customers to translate business challenges into trustworthy AI solutions, ensuring responsible AI practices throughout. You will mentor a small team, manage performance, and provide strategic direction while solving complex problems.


Minimum (essential) Requirements

  • A minimum of a 2.1 degree in Computer Science, AI, Data Science, Statistics or a similar quantitative field.
  • Deep understanding and experience developing 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 translate requirements into non‑technical language.
  • Experience managing, coaching and developing junior team members and the wider community.

Desirable

  • Demonstrable experience with modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine‑tuning or distillation of LLMs (e.g. GPT, Llama, Claude, Gemini), and ML libraries (e.g. scikit‑learn, XGBoost).
  • Experience with AI data storage, vector databases, semantic search and knowledge graphs.
  • Contributions to open‑source AI projects or research publications.
  • Familiarity with AI security, privacy and compliance standards e.g. ISO42001.

Embracing our differences

At Kainos, we believe in the power of diversity, equity and inclusion. We are committed to building a team that is as diverse as the world we live in, where everyone is valued, respected and given an equal chance to thrive. If you require accommodations or adjustments, please reach out – we are happy to support you.


We understand that everyone's journey is different, and by having a private conversation we can ensure that our recruitment process is tailored to your needs.


#J-18808-Ljbffr

Related Jobs

View all jobs

Lead Data Scientist - Remote

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.