Role overview
About the Role
An AI healthcare company that automates administrative and pharmacy workflows to help patients access life-changing medications and specialty treatments is seeking an experienced Cardiology Nurse Practitioner (NP), Registered Nurse (RN), or Physician Assistant (PA) for a part-time consulting role. The role focuses on improving an AI model that identifies patients likely to have transthyretin (ATTR) cardiac amyloidosis through clinical chart review, patient case labeling, and evidence-based clinical reasoning.
What You’ll Do
- Review patient charts and assess the likelihood of ATTR cardiac amyloidosis, including cases with incomplete information.
- Identify relevant clinical signals, including patient history and diagnostic findings such as PYP scans, where applicable.
- Document clear, evidence-based reasoning; distinguish ATTR from similar conditions and maintain consistent labeling standards.
- Refine assessments based on new evidence and AI model feedback, explain clinical decisions to technical teams, and meet labeling deadlines.
Who Should Apply
- NP, RN, or PA with 5+ years of cardiology-focused experience, ideally within a cardiology practice or clinic.
- 1–3 years of experience in a Heart Failure or ATTR-specific clinic.
- Hands-on experience evaluating or managing suspected or confirmed ATTR cardiomyopathy, including PYP scans as appropriate to professional scope.
- Strong clinical judgment and the ability to explain decisions using clear, structured criteria.
- Able to commit 10–20 hours per week alongside clinical practice.
Preferred: Experience at amyloidosis center of excellence or cardiomyopathy clinic; contributions to ATTR-related research or publications; or prior experience with AI/ML teams, clinical decision-support systems, or clinical chart abstraction and labeling.
Project Details
- Engagement: Part-time with a commitment of approximately 10–20 hours per week.
- Required role: US-based clinicians only
- Focus: Patient chart annotation, clinical reasoning, and AI model improvement.
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