Über diese Senior Research Scientist Stelle bei Whoop
Senior Research Scientist
At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP empowers users to perform at a higher level through a deeper understanding of their bodies and daily lives.
We are seeking a Senior Research Scientist to lead hypothesis-driven research that translates continuous physiological data into meaningful, real-world insights. You will work at the intersection of data science, human physiology, and product innovation, and will focus on understanding how health, behavior, and context interact over time at the individual and population level.
The ideal candidate will design and execute research that goes beyond detection to model trajectories, variability, and individual response patterns to enable more accurate and actionable insights.
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:
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
