Senior ML Data Scientist - Women’s Health

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.

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 Women’s Health team. You will play a pivotal role in developing and commercializing cutting-edge machine learning models and algorithms, leveraging wearable health data to improve women’s health outcomes. You will lead complex projects, mentor junior team members, and contribute significantly to the strategic direction of our technology.

• Contribute to the development and deployment of advanced machine learning pipelines and models for innovative Women’s Health features, focusing on time-series analysis and robust validation.
• Drive the commercialization of machine learning models and algorithms, collaborating closely with cross‑functional teams including scientists, product managers, software developers, product designers and test engineers.
• Design and execute rigorous statistical analyses for model performance evaluation, monitoring, and generating actionable insights to inform strategic product decisions.
• Conduct in-depth exploratory research to identify and characterize novel health sensing features, translating proof-of-concept ideas into scalable, commercialized products.
• Contribute to the development of best practices for machine learning within the Women’s Health team.
• Present and communicate complex technical concepts to diverse audiences.

• PhD in Biomedical Engineering, Electrical Engineering, Biostatistics, Computer Science, or a related field. And 3+ years of industry experience in machine learning and data science.
• 5+ years of advanced programming experience in Python and SQL.
• Extensive experience with a wide range of machine learning and statistical modeling techniques, including deep learning, time-series analysis, and signal processing.
• Demonstrated experience in the full lifecycle of developing and commercializing machine learning models and algorithms.
• Excellent communication skills, and the ability to articulate complex technical concepts to both technical and non-technical audiences.
• Self-driven, motivated, have a pragmatic can-do attitude and delivery-focused mindset: you can move quickly and prioritize effectively.
• Strong domain knowledge in Women’s Health and experience with health data from wearables are significant plusses.

At Oura, we care about you and your well-being. Everyone here at Oura has a ring of their own and we are continually looking to improve employee health.

• Competitive salary and equity packages
• Health, dental, vision insurance, and mental health resources
• An Oura Ring of your own plus employee discounts for friends & family
• 20 days of paid time off plus 13 paid holidays plus 8 days of flexible wellness time off
• Paid sick leave and parental leave

Oura takes a market-based approach to pay, which may vary depending on your location. US locations are categorized into tiers based on a cost of labor index for that geographic area. While most offers will be closer to the starting range, successful candidates’ pay will be determined based on job-related skills, experience, qualifications, work location, internal peer equity, and market conditions.

These ranges…