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.
We are looking for a Data Science Pod Lead to lead our physics-based sensing pod: a senior technical leader who combines deep first-principles expertise with a passion for growing people and teams. It is a dual-track role, with approximately 70% individual contribution, developing and delivering production-ready, physics-based algorithms from novel sensor data, and approximately 30% people leadership as direct line manager and mentor for a pod of data scientists. You will work across projects such as Laser Speckle and other optical imaging, turning physical sensor behaviour into validated, clinically meaningful measurements, in close collaboration with hardware and optics engineers, clinical scientists and clinicians. The role puts Member-first, always and Optimistic truth seeking into practice, and sits in the Data Science leadership team alongside other Pod Leads and the Area Lead.
Build and lead a high-performing pod of data scientists, providing line management, mentorship, regular feedback and career development support to help each team member thrive (Tech-enabled, human-centred.)
Develop, verify, validate and deploy physics-based algorithms for clinical measurement, contributing to new product capabilities or research breakthroughs that improve member outcomes (Member-first, always).
Set and uphold technical standards, code quality and best practices within the pod, and deliver production-quality code integrated into Neko's backend infrastructure, raising the bar for technical excellence across the Data Science team (Chase 10X, not 10%).
Collaborate with optical and hardware engineers, firmware engineers, software engineers, medical doctors and clinical researchers to close the gap between physical sensor behaviour and production software, design calibration, validation and noise characterisation methods, and support the regulatory readiness of algorithms (Optimistic truth seeking).
Contribute to the Data Science leadership team, shaping area-wide strategy, best practices, common tooling and ways of working alongside other Pod Leads and the Area Lead (Impact, Not Activity).
MSc or PhD in Physics, Optical Engineering, Biomedical Engineering, Electrical Engineering or relevant Physics based commercial experience, with 5+ years of relevant industry experience in a Data Scientist, Applied Scientist or comparable role at Senior, Lead, Staff level or above.
People leadership experience (line management, team lead or equivalent), with the ability to inspire and develop a technical team.
A demonstrated track record of shipping algorithms to production.
Deep knowledge of physics-based modelling and simulation, and experience with sensor data, signal processing or measurement techniques (for example laser speckle, doppler, OCT, interferometry or spectroscopy) in cross-functional R&D environments.
Strong software engineering skills (production-level coding, version control, testing, backend integration), and confidence using current AI tooling to work faster while keeping ownership of the result.
Strong communication and collaboration skills, with comfort operating under uncertainty, making pragmatic trade-offs and driving clarity in ambiguous situations.
Experience shipping solutions in a real-world product or clinical environment, from first-principles model through to validated measurement, including experience at the hardware–software boundary: characterising sensors, building calibration pipelines and validating measurements against ground truth.
Prior experience in AI-enabled healthcare, medtech, or biotech.
Systems thinking: ability to design and reason about end-to-end ML systems that are observable, safe, and scalable.
Demonstrated success mentoring engineers or researchers in high-growth, mission-driven organisations.