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 hiring a Director for our Data Science Area . Data Science sits in Neko's Data & AI function alongside ML Engineering, Data Engineering and Data Analytics, and you will be a senior voice in Data & AI leadership.
You own the technical direction of the area. What we work on is shaped by multiple factors; Product & Innovation teams, our own data and the insights generated by Data Science. You decide how Data Science delivers. This includes where Neko builds its own models and where it adapts what already exists. You hold the technical bar across projects and keep the work focused on impact for our members.
You will also be responsible for the well-being and performance of the data science team and the relationships and ways of working with internal partners. Working closely with the Pod Leads, you hire, set ways of working, and develop a team that takes research ideas through to products with real impact.
Area leadership at Neko carries real ownership. Data Science is still growing, priorities change as the company does, and the information you need is often spread across teams, from Medical Science and Innovation to Hardware and Product. You'll take the initiative to find it, drive decisions forward, and shape how the area works with the rest of Neko, with an experienced Data & AI leadership group around you.
Set the technical direction for Data Science and decide how people are deployed across projects as needs shift, working with Product and Innovation on what gets built and how. (Impact, not activity)
Expand what Data Science can take on: bring hands-on frontier and multimodal modelling experience to problems across sensor, imaging and time-series data. (Chase 10X, not 10%)
Provide senior people leadership within Data Science: develop people and strengthen how the area leads and grows its teams, in close partnership with the Pod Leads and Data & AI leadership. (Tech-enabled, human-centred)
Engage directly with the hardest technical problems across projects, lead technical review, and make the difficult calls when the team is split. (Optimistic truth seeking)
Make sure the area's work becomes validated, clinically trustworthy decision support in a regulated medical device setting. (Member-first, always)
Contribute to Data & AI leadership: strategy, hiring, and ways of working across the area.
Direct, hands-on experience taking applied research into production with measurable impact, including frontier or multimodal models on complex real-world data where you led the technical approach.
Hands-on experience across several data modalities, for example sensor data, time series, biosignals, imaging or video.
Experience working end to end and close to the data: you have worked with primary or proprietary data from how it is collected through to the model and the product.
A track record of setting technical direction across multiple teams or workstreams, moving easily between strategy and hands-on technical detail.
Substantial people-leadership experience, including developing other leaders, with a direct approach that drives decisions and delivery forward, even when the path is unclear or priorities shift.
Sound judgment on when to build or adapt foundation models versus use what already exists, and how to make that call across projects.
A strong applied research background, with the depth and credibility to work with academic and clinical research partners and the ability to traverse technical layers from high level strategy and architecture to what the teams are doing on the ground.
Experience building strong collaborations with software engineering foundation and working closely with ML and data engineering.
A PhD in Machine Learning, Computer Science, Physics, Biomedical Engineering, or a related quantitative field, or an equivalent research and delivery track record.
Experience across a mix of environments, such as startups, scale-ups and large companies, and the ability to draw on that range to apply what fits Neko, including long development cycles.
Experience in a regulated environment (medical devices, IVD, ADAS or equivalent) and familiarity with the medical device development lifecycle.
Published or externally recognised work in health AI or an adjacent frontier area, with academic or research relationships strong enough to attract others to Neko.
Hands-on experience across both building and adapting foundation models, so the build-versus-adopt judgment is grounded in having done both.
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