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Health Studies Scientist
Mindlance
- 🇺🇸 United States
- On-site
- 2 weeks ago
- $83.18 / hour
- GCP
- Machine Learning
- AI
- Python
2 weeks ago
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.
- 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.
- 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.
Health Studies Scientist · Mindlance