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Salary: USD 147,900 - 203,000 / annual
Oura is seeking a Senior Machine Learning Data Scientist to join the Health Foundation Models team. You will develop foundation models of longitudinal wearable data to unlock new health insights and product experiences. This is a researcher-builder role where you will shape an evolving research agenda, taking ambitious ideas end to end: from identifying the right question and developing new modeling approaches, through building systems to test them and rigorously evaluating results, to prototyping and shipping real-world applications.
Key responsibilities include:
- 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 datasets and experimental infrastructure
- 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 Oura members
- Share scientifically important advances through publication when warranted, without losing focus on product impact
- Collaborate with scientists, clinicians, engineers, and product partners while independently driving work through ambiguity
This is a fully remote role within the United States, with a slight preference for candidates based on the East Coast. There is no in-office requirement.
Requirements:
- 5+ years of relevant machine learning research and applied experience, including experience gained during doctoral research
- PhD or MSc in machine learning, computer science, statistics, electrical engineering, or a related quantitative field
- Exceptional research judgment: 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 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 ability to build data, training, and evaluation systems
- Strong record of first-author, peer-reviewed publications in leading machine learning, time-series, digital health, or related venues
- Clear communication, intellectual honesty, and collaborative, low-ego approach to technical disagreement and feedback
Nice to have:
- 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
- Genuine interest in using machine learning to improve health
