Neko is redefining what prevention means, from treating illness when it arrives, to sustaining health before it's ever at risk. Our mission: make data-driven, preventative care accessible to more people, before symptoms appear.
In a single, non-invasive visit under an hour, proprietary technology and direct clinical care combine to deliver personalised, actionable insights. It's a team that thinks in 10x, not 10%. Every role here plays a part in building a world where prevention is the norm, and where your work genuinely helps people live longer, healthier lives.
At Neko Health, we are building a future where healthcare helps people stay well through prevention and earlier detection. Our medical devices and AI turn new ways of measuring health into opportunities to improve care. Rigorous clinical science is what makes that translation possible.
We are seeking a Principal Clinical Scientist to own the scientific evidence pathway for our medical devices, AI algorithms and Software as a Medical Device (SaMD), from early concepts and prototypes through clinical validation, regulatory submissions and evaluation after launch.
You will join multiple R&D programmes already underway, combining scientific leadership with hands-on study design, analysis and interpretation. Working alongside engineers, data scientists, medical leads, research operations and regulatory affairs, you will determine what evidence is needed, design pragmatic studies to generate it and use the findings to guide development and clinical use.
Take ownership of active programmes. Assess the technology, clinical use cases and existing evidence across priority R&D programmes. Identify the questions and decisions that matter most for earlier detection, better care and meaningful benefits for our members (Member-first, always).
Build clear, efficient evidence plans. Define studies, milestones and criteria for progression, aligning product development with clinical and regulatory needs. Find opportunities for studies and datasets to answer multiple questions and accelerate progress across programmes (Chase 10X, not 10%).
Move studies into execution. Develop or refine protocols with appropriate populations, endpoints, reference standards and analytical approaches. Work with research operations and clinical partners to secure the necessary approvals and get studies underway.
Shape development through clinical science. Guide prototyping and data collection with engineering, data science and medical leads, connecting measurement performance and algorithm outputs to their intended use in clinical practice (Tech-enabled, human-centred).
Turn results into decisions. Deliver analyses and scientific reports from existing datasets and studies reaching completion. Challenge assumptions, investigate unexpected findings and make clear recommendations on what should progress, change or stop (Optimistic truth seeking).
Deliver evidence for regulatory review. Own the scientific content of clinical evidence packages for programmes approaching submission, working with regulatory affairs to address gaps, substantiate proposed claims and respond to reviewers.
Strengthen how the team works. Use AI tools to accelerate research, analysis and scientific writing while retaining scientific judgement and accountability. Mentor colleagues and establish reusable methods that improve the quality and pace of future programmes.
PhD, MD or equivalent doctoral qualification in a relevant clinical, scientific or quantitative discipline. A medical degree is not required.
Strong medical and physiological understanding, with the ability to connect device measurements and algorithm outputs to disease processes, clinical pathways and decisions, and critically assess their clinical relevance.
Demonstrated scientific leadership of clinical studies, with hands-on ownership from study design and protocol development through analysis, interpretation and reporting. Your work has informed product development, regulatory submissions or clinical implementation.
Strong quantitative judgement: you can define and defend sample sizes, endpoints, reference standards and analysis plans. You understand common pitfalls in diagnostic accuracy and AI validation studies, including selection bias, data leakage and inappropriate comparators, and how to address them.
Hands-on fluency with AI and data, including practical proficiency in Python or R, an understanding of how modern machine learning and foundation models are developed and evaluated, and regular use of large language models in your work.
A working understanding of how evidence maps to regulatory and ethics pathways, including FDA and UK MHRA requirements, EU MDR and ISO 14155. You can translate these into appropriate study designs in partnership with regulatory affairs specialists.
Experience in a fast-moving, product-led environment, such as a technology company, medtech scale-up or applied research lab, and a clear preference for working this way. You can work independently, prioritise across programmes and make sound scientific decisions under uncertainty.
Clear communication with scientific, clinical, technical and executive audiences, including the ability to explain findings, limitations and their implications for development and clinical use.
Experience developing and validating medical devices, diagnostics or medical AI.
Experience with physiological sensing, medical imaging, novel biomarkers or multimodal diagnostic technologies.
Experience with clinical studies across multiple sites or countries.
Experience mentoring scientists and building productive collaborations with academic and clinical partners.