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