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Senior Research Scientist at WHOOP

Senior Research Scientist
WHOOP
On-site
Boston, MA
Full-time
Salary not listed
Posted 11 May 2026
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Job Description

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

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