Senior ML Data Scientist, Health Sensing

About the role

Our mission at Oura is to empower every person to own their inner potential. Our award-winning products help our global community gain a deeper knowledge of their readiness, activity, and sleep quality by using their Oura Ring and its connected app. We've helped millions of people understand and improve their health by providing daily insights and practical steps to inspire healthy lifestyles. Empowering the world starts with living our values and empowering our team. As a quickly growing company focused on helping people live healthier and happier lives, we ensure that our team members have what they need to do their best work — both in and out of the office. With our award-winning Oura Ring and app, we help over 2.5 million people turn insights about sleep, activity, and readiness into healthier, more balanced lives. We believe that starts from within - by creating a culture where our team feels supported, included, and inspired to do their best work. Our values guide how we show up for each other and our community every day. This is a remote US role with a preference for candidates based in San Francisco, San Diego, or Boston. We have offices in San Francisco and San Diego for those who prefer hybrid or office settings. Oura employees in other major cities (like Boston and New York) occasionally gather informally at local co-working locations. We are seeking a highly experienced and driven Senior Machine Learning Data Scientist to join our health sensing team. You will play a pivotal role in developing and commercializing cutting-edge machine learning models and algorithms, leveraging wearable health data to help members detect and understand illness. This role carries a high degree of independence — you will own projects end to end, set your own technical direction, and partner directly with product and engineering to bring your work to members.

Responsibilities

• Contribute to the development and deployment of advanced machine learning pipelines and models for innovative illness detection features, focusing on time-series analysis and robust validation.
• Drive the commercialization of machine