Senior Research Scientist

RESPONSIBILITIES: - - Lead end-to-end research projects from hypothesis formulation through analysis, interpretation, and communication - Analyze large-scale, longitudinal physiological and behavioral datasets to identify meaningful patterns and insights - Develop and evaluate models that characterize individual variability and predict future physiological states - Translate research findings into clear, actionable recommendations that inform product direction and algorithm development - Collaborate closely with product, engineering, and data science teams to ensure research is interpretable and aligned with real-world use cases - Contribute to the design and execution of research programs - Produce high-quality scientific outputs, including internal reports, white papers, and peer-reviewed publications - Serve as a senior technical leader, providing guidance and mentorship to junior scientists and contributing to raising the bar for scientific rigor across the team. - Help define research standards, methodologies, and best practices across the team QUALIFICATIONS: - - PhD (or equivalent experience) in a quantitative or health-related field (e.g., Epidemiology, Biostatistics, Biomedical Engineering, Neuroscience, Computer Science, or related disciplines),  - Strong background in health science, with grounding in public health and clinical concepts, and experience modeling longitudinal or time-series data (e.g., within-person variability in real-world settings) - Demonstrated ability to design hypothesis-driven analyses and translate findings into clear conclusions - Proficiency in statistical modeling and/or machine learning methods and demonstrated experience using Python or R - Significant hands-on experience with advanced modeling techniques for longitudinal/time-series data, such as probabilistic methods, Bayesian inference, and/or causal inference - Ability to work across disciplines and communicate effectively with both technical and non-technical stakeholders - Experience connecting data analysis to real-world applications (product, wellness, clinical, or operational) - Strong written and verbal communication skills