Responsibilities
- Design and lead real-world and clinical research studies, including observational, retrospective, prospective, and interventional designs, ensuring methodological rigor and scientific integrity.
- Develop and execute statistical analysis plans, including use of mixed models, causal inference, and other advanced methods, to generate insights on cardiometabolic health and behavior change.
- Conduct and interpret analyses of large-scale time-series physiological and wearable data to uncover meaningful patterns and health outcomes.
- Prepare IRB submissions and oversee compliance when applicable to support ethical and regulatory standards in research execution.
- Communicate research findings through peer-reviewed publications, scientific presentations, white papers, internal briefs, and cross-functional knowledge sharing.
- Establish and manage academic and industry research partnerships to support validation studies, innovation initiatives, and scientific advancement.
- Collaborate with internal stakeholders across Marketing, Data Science, Enterprise, and Product teams to translate research into product and business impact.
- Mentor junior scientists, fostering a culture of scientific rigor, curiosity, and collaboration within the Performance Science team.
Qualifications
- Advanced degree (PhD, MD, PharmD, or equivalent) in life sciences, public health, biostatistics, physiology, cardiometabolic health, biomedical data science, or related discipline.
- 5+ years of post‑graduate experience with a strong record of publications and independently led research.
- Demonstrated experience designing and executing clinical research and real‑world studies; familiarity with IRB processes and clinical trial frameworks.
- Advanced proficiency in Python, R, and/or SQL for data extraction, statistical analysis, and visualization of wearable and biomedical datasets.
- Experience working with large‑scale time‑series physiological or wearable data.
- Ability to communicate scientific insights effectively with both technical and non‑technical audiences.
- Excellent organizational skills, attention to detail, and ability to manage multiple projects.
- Experience collaborating with academic partners, healthcare systems, or consortiums.
- Strong commitment to embracing and leveraging AI tools in day‑to‑day tasks, ensuring AI‑assisted work aligns with the same high‑quality standards as personal contributions.
