Oura is looking to develop and refine data-driven methods to understand and quantify the impact of sleep, recovery, and daily behaviors on long-term health outcomes, bridging wearable data with clinical insights to shape how individuals and healthcare providers understand wellness, prevention, and recovery.
Requirements
- Strong experience with time-series physiological data (e.g., heart rate, HRV, respiration, sleep, actigraphy) and/or clinical datasets.
- Advanced level proficiency in Python (scientific/ML libraries, modular code design, testing, performance optimization).
- Strong proficiency in SQL and data wrangling, with the ability to build and maintain robust research-grade data pipelines.
- Familiarity with end-to-end development on cloud platforms (AWS or similar) for scaling data workflows.
- Proven ability to collaborate with clinical researchers, interpret study protocols, and work with real-world evidence datasets.
- Familiarity with regulatory standards for digital health validation (e.g., FDA, EMA, HIPAA).
- Background in Value Based Care/Population Health data and metrics including Healthcare Effectiveness Data and Information Set (HEDIS) and similar reporting across quality and services for patient, preventive care, chronic condition management including interventions, benchmarking, and quality improvements
Responsibilities
- Designing evidence frameworks and validating algorithms that link wearable sensor data to meaningful clinical outcomes (e.g., cardiovascular, metabolic, and mental health indicators).
- Developing models to assess the impact of sleep, recovery, and activity patterns on population-level health and individual well-being.
- Partnering with clinical researchers to design, analyze, and interpret studies that validate and communicate the health and economic impact of wearable-derived metrics
- Improving signal extraction from physiological time-series data (e.g., HRV, sleep staging, temperature) to make them more clinically interpretable.
- Working cross-functionally with product, research, and clinical teams to translate scientific insights into product features and healthcare applications.
- Contributing to publications and presentations that advance the clinical credibility of wearable-derived insights.
Other
- 3+ years of relevant work experience, ideally with exposure to clinical or health outcomes research, as a data scientist, research scientist, or ML engineer (including end-to-end deployment of models into research or product settings).
- Have a PhD in biomedical engineering, electrical engineering, biostatistics, computer science or a related field, OR a Master degree in related fields with 3+ years of relevant work experience is preferred.
- Background in EHR and clinical, provider, and payor claims data
- Background in biostatistics, epidemiology, or clinical research methods.
- Experience working in the consumer wearable or digital health space.
