Staff Clinical Scientist

Responsibilities

• Provide scientific leadership across complex clinical development and evidence-generation programs, defining research strategies that support WHOOP’s product, clinical, regulatory, and business objectives.
• Independently design and lead complex clinical studies, including research questions, protocols, endpoints, study procedures, sample-size approaches, statistical analysis plans, and interpretation frameworks.
• Translate ambiguous or novel product and clinical questions into rigorous research approaches, recommending study designs that balance scientific rigor, operational feasibility, regulatory requirements, timelines, and business needs.
• Lead the scientific strategy for study execution from feasibility and startup through data collection, analysis, interpretation, and closeout, partnering closely with Clinical Operations, Data Management, CROs, investigators, and external statisticians.
• Lead statistical strategy and analysis of clinical, wearable, and real-world datasets, critically evaluating methodology, assumptions, bias, missing data, sensitivity analyses, subgroup analyses, and limitations.
• Establish scientific standards and provide expert review of study designs, statistical analysis plans, analyses, outputs, and conclusions generated by Clinical, Data Science, and external partners.
• Lead literature reviews and evidence synthesis, integrating published research, WHOOP data, and real-world evidence to identify evidence gaps and inform product strategy, clinical claims, study design, regulatory strategy, and future research priorities.
• Ownt or provide senior scientific oversight for protocols, statistical analysis plans, clinical study reports, clinical evidence summaries, regulatory submission sections, manuscripts, abstracts, and other scientific communications, ensuring accuracy, rigor, and regulatory defensibility.
• Serve as a senior scientific partner to Product, Data Science, Engineering, Regulatory Affairs, Quality, and Clinical Operations, translating clinical evidence into clear recommendations for product development, algorithms, claims, regulatory strategy, and member experience.
• Raise the scientific bar across the Clinical organization by mentoring Clinical Scientists and Senior Clinical Scientists, identifying scientific risks and opportunities, and contributing to WHOOP’s longer-term clinical research and evidence-generation roadmap.

Qualifications

• Advanced degree in Life Sciences, Clinical Research, Public Health, Epidemiology, Biostatistics, Biomedical Engineering, Medicine, or a related field; PhD, MD, PharmD, ScD, DrPH, or equivalent advanced scientific training preferred.
• Approximately 9–12+ years of relevant experience in clinical research, clinical science, medical devices, digital health, SaMD, pharmaceutical, biotech, or related health research environments.
• Demonstrated track record of independently leading complex clinical research programs from study design through analysis, interpretation, and reporting, including defining scientific approaches for ambiguous or novel questions.
• Deep expertise in clinical study design, endpoint development, statistical methodology, and interpretation of clinical evidence.
• Strong proficiency in R, SAS, Python, Stata, or similar tools, with the ability to independently analyze complex clinical datasets and critically review analyses performed by others.
• Strong understanding of clinical research standards and regulated evidence generation, including ICH-GCP, FDA expectations, ISO 14155, and experience contributing to regulatory submissions or clinical evidence packages.
• Excellent scientific writing and communication skills, with experience translating complex clinical and statistical findings into clear recommendations for technical, product, regulatory, and executive audiences.
• Demonstrated ability to influence cross-functional decisions, operate independently with strong scientific judgment, and mentor or technically guide other scientists.
• Nice to have: Experience with wearable data, digital biomarkers, real-world data, SaMD, machine-learning validation, or large-scale longitudinal health datasets.