Data Science Pod Lead (Machine Learning)

Location: Hybrid in Stockholm, Berlin, or London

Company

A healthcare technology company developing non-invasive, data-driven preventative care through proprietary technology and direct clinical care.

What you will do

• Develop, verify, validate, and deploy machine learning models and algorithms for clinical decision support.
• Build and lead a high-performing pod of data scientists through line management, mentorship, feedback, and career development.
• Set technical standards, code quality practices, and best practices while delivering production-quality code integrated with backend infrastructure.
• Collaborate with hardware, firmware, and software engineers, medical doctors, and clinical researchers on clinical use cases and regulatory readiness.
• Contribute to Data Science strategy, shared tooling, quality standards, and ways of working alongside other technical leads.

Requirements

• MSc or PhD in Machine Learning, Computer Science, Physics, Biomedical Engineering, or a related quantitative field.
• At least 5 years of relevant industry experience in Data Science, ML Engineering, or Applied Science, or at least 2 years of comparable experience after a PhD.
• Proven experience shipping algorithms or machine learning 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 machine learning lifecycle.
• Strong production software engineering skills, including coding, version control, testing, and backend integration.
• Experience with sensor data, time-series analysis, or medical imaging, plus people leadership and cross-functional collaboration experience.

Nice to have

• Experience in regulated environments such as medical devices or IVD, with familiarity with regulatory requirements for medical algorithms.
• Background in AI-enabled healthcare, medtech, or biotech.
• Ability to design observable, safe, and scalable end-to-end machine learning systems.
• Experience mentoring engineers or researchers in high-growth, mission-driven organisations.

Culture & Benefits

• Work at the intersection of machine learning and preventative healthcare.
• Collaborate across data science, engineering, clinical, and research disciplines.
• Balance approximately 75% individual contribution with 25% people leadership.
• Contribute to technology intended to improve early detection and member health outcomes.