Data Science Lead (CV + ML)

Kurz zusammengefasst

Neko is a health‑technology company that aims to shift care from treatment to prevention. Using a proprietary, non‑invasive body‑scan that captures detailed imaging in under an hour, Neko delivers personalized, data‑driven insights through its clinics and products. The Data Science Lead (CV + ML) will join a multidisciplinary team that includes hardware engineers, software engineers, clinical scientists and other data scientists, all focused on turning rich imaging data into validated, production‑ready algorithms with real clinical impact. In the first six to twelve months the role will involve designing, testing and deploying computer‑vision and machine‑learning models that support clinical decision‑making and add new product features. You will own projects from ambiguous problem statements and noisy real‑world data through modelling, evaluation and full production deployment, adapting to shifting priorities. Collaboration is key: you will work closely with engineers and clinicians to bridge the gap between sensor capture and clinic‑ready solutions, applying classical, geometric and deep‑learning methods, often with semi‑, self‑ or weakly‑supervised techniques when expert labels are scarce. Rigorous evaluation, analysis of clinical study data and support for regulatory readiness are also part of the remit, as is delivering clean, production‑grade Python code integrated into Neko's backend infrastructure. The position requires extensive experience in modern computer‑vision and deep‑learning on real‑world camera or sensor data, including detection, segmentation, tracking or 3D perception, and the judgment to choose classical or geometric approaches when appropriate. Candidates must have a proven track record of making algorithms robust to challenging capture conditions, such as variable lighting, motion, optics and calibration, and shipping them to production. Strong fundamentals in machine learning, algorithmic thinking, and the ability to quickly grasp new problem types are essential, together with solid software‑engineering skills in Python, including testing and code review. The role demands at least five years of relevant industry experience or two years post‑PhD, experience in cross‑functional R&D teams, clear communication across technical and clinical audiences, and confidence in using the latest AI tooling. An MSc or PhD in a related field and a motivation to apply science to preventative healthcare are also required. Preferred, but not mandatory, experience includes work with multi‑view geometry, 3D vision or reconstruction, label‑efficient learning, large‑scale data pipelines, sensor fusion, non‑RGB imaging (e.g., thermal) and exposure to safety‑critical or regulated environments. Mentoring or teaching engineers and researchers in fast‑growing, mission‑driven organisations is also valued. What the role asks for: - 5+ years industry experience or 2+ years post‑PhD - MSc or PhD in Computer Vision, ML, CS, Physics, Engineering or related field - Strong computer‑vision and deep‑learning experience on real‑world sensor data - Proven ability to ship robust algorithms to production under messy conditions - Solid Python software‑engineering skills with production‑level code and testing - Experience in cross‑functional R&D teams with hardware and software engineers - Excellent communication with clinicians, engineers and managers - AI fluency with latest tooling for productivity - Nice‑to‑have: multi‑view geometry, 3D vision, label‑efficient learning, sensor fusion, non‑RGB imaging, safety‑critical/regulatory exposure, mentoring experience.