Related skills
r python machine learning causal inference bayesian inferenceπ Description
- Lead end-to-end research projects from hypothesis to communication
- Analyze large-scale longitudinal physiological and behavioral datasets
- Develop and evaluate models that characterize variability and predict future states
- Translate findings into actionable recommendations informing product direction and algorithms
- Collaborate with product, engineering, and data science teams to ensure research is interpretable
- Produce high-quality scientific outputs: internal reports, white papers, and peer-reviewed publications
π― Requirements
- PhD (or equivalent) in a quantitative or health-related field
- Strong background in health science with grounding in public health and clinical concepts, modeling longitudinal or time-series data
- 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 (probabilistic methods, Bayesian inference, causal inference)
- Ability to work across disciplines and communicate effectively with technical and non-technical stakeholders
π Benefits
- Relocation assistance to Boston, MA
- Equity package and competitive benefits
- Growth and mentorship in a senior technical leadership role
- Collaborative, cross-functional team with product, engineering, and data science
- Opportunity to publish research in internal reports, white papers, and peer-reviewed publications
- Inclusive, supportive workplace culture
π Relocation support
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