Health Studies Scientist

Job-ID Reference Salary$83.18 / hour Health Studies Scientist Job Description Role Overview As a Health Studies Scientist, you will partner with cross-functional engineering and product teams to design, pilot, and execute health studies that evaluate sensor and algorithm performance. You will translate research goals into rigorous protocols, lead hands-on data collection with human participants, and ensure high scientific validity and data integrity. Key Responsibilities Study Design & Protocols: Collaborate with engineers to define hypotheses, endpoints, and methodologies for lab and remote-based longitudinal studies. Author study protocols, Informed Consent Forms (ICFs), and SOPs that comply with GCP and human subject protections. Hands-on Operations: Coordinate and lead study sessions involving human participants, wearable sensors, internal data logging tools, and third-party clinical reference devices. Pilot & Feasibility Testing: Lead pilot studies to de-risk protocols, validate collection pipelines, and troubleshoot sensor performance before scaling. Cross-Functional Execution: Partner with Study Operations Engineers to launch, monitor, and close out studies; train personnel on collection protocols and data-quality standards. Required Qualifications Minimum Education: Bachelor’s degree in biomedical engineering, Bioengineering, Health Sciences, or a related field. Experience: 3+ years of experience in health/clinical research, sensor validation, or human data collection. Protocol Development: Demonstrated experience authoring research protocols, consent documents, and study documentation. Clear communicator who can align priorities across research scientists, engineers, legal, privacy, and operations teams. Preferred Skills Familiarity with FDA regulatory standards, GCP, human factors principles and project management Operations & Participant Interaction: Direct experience conducting studies with human participants, operating physiological sensors/devices, and managing data collection workflows. GenAI / Technical Tools: Hands-on experience using Generative AI tools (e.g., LLM-assisted coding, prompt engineering, or AI productivity workflows) to build tools, automate operational tasks, or accelerate scientific documentation. Experience using Python or shell scripts to verify, check, and process collected data. Mindlance