Data Science Lead - Computer Vision & Machine Learning

Company Mission

Neko Health is transforming the concept of prevention by shifting from treating diseases after they arise to maintaining health proactively before symptoms occur. Their goal is to provide accessible, data-driven preventive care that empowers more individuals to stay healthy.

Role Objective

The core task involves developing clinically validated, production-ready algorithms from extensive imaging data obtained through Neko’s body scanning technology. This position focuses on computer vision challenges including skin and tissue imaging and body measurement, collaborating closely with hardware, software, and clinical teams to prototype solutions, validate clinical applications, and deploy machine learning models integrated within Neko's healthcare services.

Key Responsibilities

• Create, validate, and deploy innovative computer vision and machine learning algorithms to assist clinical decision-making and enhance product features for improved patient outcomes.
• Take ownership of projects from ambiguous problems and raw data to fully functional production models, adapting to shifting priorities.
• Work alongside multidisciplinary teams—hardware engineers, software developers, medical professionals, and clinical researchers—to bridge sensor data capture and clinical application.
• Integrate classical geometric methods with contemporary machine learning approaches such as semi-, self-, and weakly-supervised techniques where labeled data is limited, ensuring rigorous evaluation and supporting regulatory compliance through clinical data analysis.
• Develop robust production-grade code integrated into Neko’s backend systems, leveraging AI tools for efficiency without compromising quality or responsibility.
• Mentor peers, conduct code and design reviews, and foster a culture of transparency and ongoing enhancement.

Required Qualifications and Experience

• Deep expertise in modern computer vision and deep learning techniques applied to real-world sensor data, including detection, segmentation, tracking, and 3D perception, with prudent choice between classical and geometric solutions.
• Proven success delivering algorithms that perform reliably under challenging real-world conditions like varying lighting, motion artifacts, optics, and calibration, with deployment experience.
• Strong foundation in machine learning principles and diverse algorithmic problem solving with the ability to adapt quickly to new challenges and evaluation methods.
• Advanced proficiency in Python programming, emphasizing production-quality code, testing procedures, and peer reviews.
• At least five years of relevant industry experience or a minimum of two years of experience following a PhD completion.
• Experience collaborating within interdisciplinary R&D environments involving hardware and software engineering teams.
• Excellent communication skills to effectively convey complex trade-offs to clinicians, engineers, and management.
• Adeptness in modern AI tooling to boost productivity and operational effectiveness.
• A Master’s or Doctoral degree in Computer Vision, Machine Learning, Computer Science, Physics, Engineering, or related disciplines.
• Strong motivation to leverage scientific expertise in advancing preventive healthcare.

Preferred Skills and Experience

• Knowledge of multi-view geometry applications including tracking, registration, calibration, SLAM, and re-identification.
• Experience in 3D vision and reconstruction technologies.
• Familiarity with label-efficient learning strategies such as semi-supervised, self-supervised learning, and handling large-scale data pipelines.
• Expertise in sensor fusion, state estimation, filtering techniques, or non-visible spectrum imaging like thermal imaging.
• A background in mentoring or teaching technical staff within mission-driven and rapidly growing teams.
• Experience or willingness to learn aspects of regulated and safety-critical environments.