Data Science Pod Lead

Role Purpose

We are looking for a Data Science Pod Lead to join our Data Science team - a senior technical leader who combines deep hands-on expertise with a passion for growing people and teams. This is a dual-track role: approximately 75% of your time will be spent as an individual contributor, developing and delivering production-ready algorithms and ML models from novel sensor data, while approximately 25% will be dedicated to people leadership, serving as the direct line manager and mentor for a pod of data scientists.

You will work across projects such as Laser Speckle Imaging, contactless ECG, Skin imaging, Thermal Imaging, Cardiovascular Algorithms, and Tissue Imaging - turning complex health data into validated, clinically impactful decision support. You will also shape the technical direction and quality standards of your pod, contribute to the broader Data Science leadership team, and collaborate closely with engineers, clinicians, and researchers to bring algorithms and models from prototype to production.

If you are motivated by the intersection of cutting-edge machine learning and preventative healthcare, and you want to lead a team while staying close to the technical work, this role is for you.

What You’ll Deliver in the First 6–12 Months

• Develop, verify, validate, and deploy machine learning models and algorithms for clinical decision support, contributing to new product features or research breakthroughs that improve member outcomes (Member-first, always).

• Build and lead a high-performing pod of data scientists — providing line management, mentorship, regular feedback, and career development support to help each team member thrive (Tech-enabled, human-centred).

• Set and uphold technical standards, code quality, and best practices within the pod, and deliver production-quality code integrated into Neko’s backend infrastructure, raising the bar for technical excellence across the Data Science team (Chase 10X, not 10%).

• Collaborate cross-functionally with hardware engineers, firmware engineers, software engineers, medical doctors, and clinical researchers to develop and validate clinical use-cases, and support the regulatory readiness of algorithms and models (Optimistic truth seeking)

• Contribute to the Data Science leadership team — shaping area-wide strategy, best practices, common tooling, and ways of working alongside other Pod Leads and the Area Lead.

Requirements

• MSc or PhD in Machine Learning, Computer Science, Physics, Biomedical Engineering, or a related quantitative field.

• 5+ years of relevant industry experience in a Data Scientist, ML Engineer, or Applied Scientist role, or 2+ years post-PhD in a comparable position.

• Demonstrated track record of shipping algorithms or ML models to production in a real-world product or clinical environment.

• Deep expertise in machine learning, signal processing, or computer vision, with hands-on experience across the full ML lifecycle.

• Strong software engineering skills: production-level coding, version control, testing, and integration with backend systems.

• Experience working with sensor data, time-series analysis, or medical imaging in cross-functional R&D environments.

• People leadership experience (e.g. line management, team lead, or equivalent), with the ability to inspire and develop a technical team.

• Strong communication and collaboration skills, with comfort operating under uncertainty, making pragmatic trade-offs, and driving clarity in ambiguous situations.

• Motivated to apply cutting-edge science to improve preventative and early-detection healthcare.

Preferred

• Experience working in a regulated environment (e.g. medical devices, IVD, or equivalent) and familiarity with regulatory requirements for medical algorithms.

• Prior experience in AI-enabled healthcare, medtech, or biotech.

• Systems thinking: ability to design and reason about end-to-end ML systems that are observable, safe, and scalable.

• Demonstrated success mentoring engineers or researchers in high-growth, mission-driven organisations.