Data Science Pod Lead (MedTech)

Location: Hybrid in Stockholm, Berlin, or London

Company

hirify.global develops non-invasive, data-driven preventative healthcare technology that combines proprietary technology with 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.
• Support regulatory readiness and contribute to the Data Science leadership team's strategy, tooling, and ways of working.

Requirements

• MSc or PhD in Machine Learning, Computer Science, Physics, Biomedical Engineering, or a related quantitative field.
• 5+ years of relevant industry experience in data science, ML engineering, or applied science, or 2+ years of comparable experience after a PhD.
• 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 ML 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.
• Experience in AI-enabled healthcare, medtech, or biotech.
• Ability to design observable, safe, and scalable end-to-end ML systems.
• Success 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.
• Contribute to technology intended to improve preventative and early-detection healthcare outcomes.
• Balance approximately 75% individual-contributor work with 25% people leadership.