# Senior Research Scientist

**Company:** [WHOOP](https://hotfix.jobs/companies/whoop)
**Location:** Boston, MA
**Role:** Data Science
**Skills:** Python, R, Statistical Modeling, Machine Learning, Bayesian Inference, Causal Inference, Time-Series Analysis, Longitudinal Data Modeling, Probabilistic Methods
**Posted:** 2026-05-11

> Leads hypothesis-driven research using large-scale physiological data to model individual variability, predict states, and inform product insights. Collaborates with product and engineering teams, requiring PhD in quantitative field and expertise in stats/ML for time-series data.

## 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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