Note: The job is a remote job and is open to candidates in USA. ŌURA develops wearable health products that help people understand and improve their readiness, activity, and sleep. The Senior Machine Learning Data Scientist will lead end-to-end research on foundation models for longitudinal wearable and physiological data, from hypothesis development and modeling through rigorous evaluation, prototyping, and product delivery.
Responsibilities
- Shape and lead ambitious research directions for foundation models of longitudinal wearable and physiological data
- Own research end to end: turn open-ended questions into testable hypotheses, develop new modeling approaches, and build the datasets and experimental infrastructure needed to investigate them
- Establish rigorous evaluation methods that distinguish meaningful model capabilities from results driven by leakage, confounding, or fragile benchmarks
- Advance capabilities such as representation learning, forecasting, personalization, multimodal modeling, and generative modeling across large longitudinal datasets
- Drive promising model capabilities from research result to prototype, validation, and shipped health experiences for Ōura members
- Share scientifically important advances through publication when the work warrants it, without losing focus on product impact
- Collaborate with scientists, clinicians, engineers, and product partners while independently driving work through ambiguity
Skills
- * 5+ years of relevant machine learning research and applied experience, including experience gained during doctoral research, and a PhD or MSc in machine learning, computer science, statistics, electrical engineering, or a related quantitative field
- * Exceptional research judgment: a track record of identifying important questions, forming original hypotheses, and designing experiments that produce credible evidence
- * Deep technical expertise in modern foundation models, representation learning, large-scale training, and model evaluation
- * Demonstrated autonomy in taking ambiguous research problems from initial idea to a working system and clear scientific result
- * Strong grounding in probability, statistics, experimental design, and robust evaluation, including careful reasoning about confounding, leakage, and generalization
- * Advanced proficiency in Python and modern ML frameworks such as PyTorch or JAX, with the ability to build the data, training, and evaluation systems needed to pursue your own ideas
- * A strong record of first-author, peer-reviewed publications in leading machine learning, time-series, digital health, or related venues
- * Clear communication, intellectual honesty, and a collaborative, low-ego approach to technical disagreement and feedback
- This is a remote role within the United States, with a slight preference for candidates based in New York or elsewhere on the East Coast. There is no in-office requirement
- * Experience with longitudinal time-series, wearable sensors, physiological signals, or other real-world health data
- * Expertise in multimodal learning, generative modeling, forecasting, personalization, efficient or on-device models, or ML systems
- * Experience translating research advances into prototypes or production product capabilities
- * Familiarity with clinical research, causal inference, or evaluation using research and clinical datasets
- * 5+ years of relevant machine learning research and applied experience, including experience gained during doctoral research, and a PhD or MSc in machine learning, computer science, statistics, electrical engineering, or a related quantitative field
- * Genuine interest in using machine learning to improve health rather than applying foundation models as an end in themselves
Benefits
- Equity packages
- Health, dental, vision insurance, and mental health resources
- An Ōura 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
- Remote role within the United States
- There is no in-office requirement
Company Overview
Company H1B Sponsorship