Data Science Lead
Confirmed on the employer's own hiring board on Oct 26, 2026. First seen by Alion on Aug 10, 2026. Neko Health scores A on the Alion truth index.
Mission
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.
Role Purpose
We are hiring a Data Science Lead to join our Data Science team, working across projects such as Laser Speckle and other optical imaging. These roles take novel sensor data streams and turn them into validated, production-ready algorithms with real clinical impact. You will collaborate with hardware and optics engineers, clinical scientists and other data scientists to develop prototypes, validate clinical use cases and deliver algorithms into production, integrated into Neko Health's clinics and products. The role puts Member-first, always and Optimistic truth seeking into practice.
What You'll Deliver in the First 6-12 Months
Develop, verify, validate and deploy physics-based algorithms for laser measurement systems, contributing to new product capabilities or research breakthroughs that improve member outcomes (Member-first, always).
Collaborate with optical engineers, hardware developers and clinical researchers to deliver algorithms that bridge the gap between physical sensor behaviour and production software in Neko Health's clinics and products (Tech-enabled, human-centred).
Deliver production-quality code and integrate physics-aware algorithms into Neko's backend infrastructure, raising the bar for technical excellence across the Data Science team (Chase 10X, not 10%).
Design and execute calibration, validation and noise characterisation methods for measurement algorithms, so our science meets the highest standards of metrological and clinical rigour (Optimistic truth seeking).
Mentor and collaborate within a high-performing, multidisciplinary team, contributing to a culture of openness and continuous improvement (Optimistic truth seeking).
Requirements
MSc or PhD in Physics, Optical Engineering, Biomedical Engineering, Electrical Engineering or a related field, with 7+ years of relevant industry experience, or 5+ years post-PhD.
Deep knowledge of physics-based modelling and simulation: translating physical systems (optical, acoustic or electromagnetic) into code from first principles.
Experience with laser-based measurement techniques such as laser speckle, doppler, OCT, interferometry, lidar FMCW or spectroscopy, or related fields.
Strong software engineering skills and proven experience shipping algorithms to production.
Experience working at the hardware-software boundary: characterising sensors, building calibration pipelines and validating measurements against ground truth.
AI fluent: confident using current AI tooling to increase productivity and efficiency.
Preferred
Experience working in clinical or regulated environments, and an understanding of regulatory requirements for medical device algorithms.
Prior experience in med-tech, biophotonics, metrology or sensor-driven R&D where understanding the underlying physics was essential to getting the algorithm right.
Demonstrated success mentoring engineers or researchers in high-growth, mission-driven organisations.
Organization and Way of Working
The engineering team is divided into smaller cross functional project teams that each focus on a specific goal or target, where some groups are long-lived, and some are short-lived, depending on how big the goal or deliverable is. We strive to create groups which are cross-functional and able to complete their goals without dependence on other teams, even though this is of course not always possible.
Groups track goals on a yearly and quarterly basis with goal follow-up across the entire engineering organization on a bi-weekly basis. Most groups do internal planning on a bi-weekly basis, but in the end it's up to the group to decide how they want to work.
We have, however, mandated that all groups must present their progress or failures or hacks at our bi-weekly engineering demo, a fun meeting/presentation where we talk about everything from short-circuiting power-modules, how hard it is to calibrate cameras or align polygons in space, to neat new command line tools for operations, a new auth mechanism in the backend, a cool new way to visualize health data or a new feature which helps our doctors be more productive.
We have a flexible workplace that focuses on work/life balance, and we strongly believe in our mission but do not think that achieving it requires sacrificing everything else.