Neko Health
Data Science Lead (CV + ML)
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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
Neko’s body scan captures rich imaging data across the human body. Turning it into validated, production-ready algorithms with real clinical impact is the job. We are hiring Senior Data Scientists to work across computer vision problems such as skin imaging, tissue imaging and body measurement [TBC: confirm active project areas].
You will collaborate with hardware engineers, software engineers, clinical scientists and other data scientists to develop prototypes, validate clinical use cases, and deliver algorithms and ML models into production, integrated into Neko Health’s clinics and products.
We are looking for a broad computer vision background: people who are as comfortable with geometry, 3D and classical image processing as with modern deep learning, and who know which to reach for.
What You’ll Deliver in the First 6–12 Months
- Develop, verify, validate and deploy computer vision and ML algorithms for clinical decision support, and contribute to new product features or research breakthroughs that improve member outcomes (Member-first, always).
- Own problems end to end, from a vague question and messy real-world data, through modelling and evaluation, to a model running in production, across problem areas as priorities evolve (Chase 10X, not 10%).
- Work with hardware engineers, software engineers, medical doctors and clinical researchers to close the gap between what the sensors capture and what runs in the clinic (Tech-enabled, human-centred).
- Combine classical, geometric and learned methods, using semi-, self- or weakly-supervised approaches where expert labels are scarce, and evaluate them rigorously. Analyse clinical study data and support regulatory readiness (Optimistic truth seeking).
- Deliver production-quality code and integrate it into Neko's backend infrastructure, using AI tools to move faster while keeping the judgment and ownership your own.
- Share what you know. Mentor colleagues, review code and designs, and help build a culture of openness and continuous improvement
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Requirements
- Extensive experience in modern computer vision and deep learning on real-world camera or sensor data, such as detection, segmentation, tracking or 3D perception, with the judgment to know when a classical or geometric method is the better tool.
- A track record of making algorithms work under messy, real-world capture conditions, such as lighting, motion, optics and calibration, and shipping them to production.
- Strong machine learning fundamentals and algorithmic thinking, with breadth beyond one niche. You pick up a new problem type quickly and know how to evaluate it properly.
- Strong software engineering skills in Python, with production-level code, testing and code review.
- 5+ years of relevant industry experience, or 2+ years post-PhD.
- Experience working in cross-functional R&D teams alongside hardware and software engineers.
- Clear communication. You can explain a trade-off to a clinician, an engineer and a manager.
- AI Fluent - confident in using latest tooling developments to increase productivity and efficiency
- MSc or PhD in Computer Vision, Machine Learning, Computer Science, Physics, Engineering or a related field.
- Motivated to apply strong science to improve preventative healthcare.
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Preferred
- Experience with multi-view geometry or matching across views and time, such as tracking, registration, calibration, SLAM or re-identification.
- Experience with 3D vision or reconstruction.
- Experience with label-efficient learning (semi-supervised, self-supervised, pseudo-labelling) and large-scale data pipelines.
- Experience with sensor fusion, state estimation or filtering, or with non-RGB imaging such as thermal.
- Experience teaching or mentoring engineers or researchers in high-growth, mission-driven organisations.
- Exposure to safety-critical or regulated settings. We'll teach you the rest.
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