Senior Machine Learning Data Scientist
New
O
OuraHealth Technology
Remote - United States, East Coast preferenceFull-TimeSenior
SalaryRegion 1: $172,550 - $203,000; Region 2: $158,950 - $187,000; Region 3: $147,900 - $174,000
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonMachine LearningPyTorchData science
Requirements
- 5+ years of relevant machine learning research and applied experience.
- PhD or MSc in machine learning, computer science, statistics, electrical engineering, or a related quantitative field.
- Exceptional research judgment with a track record of identifying important questions, forming hypotheses, and designing experiments.
- Deep technical expertise in modern foundation models, representation learning, large-scale training, and model evaluation.
- Advanced proficiency in Python and modern ML frameworks such as PyTorch or JAX.
- Strong grounding in probability, statistics, experimental design, and robust evaluation.
- A strong record of first-author, peer-reviewed publications in machine learning, time-series, or digital health venues.
- Demonstrated autonomy in taking ambiguous research problems from idea to working system.
- Clear communication, intellectual honesty, and a collaborative approach.
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.
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