Clinical Data Specialist (AI)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
TL;DR
Clinical Data Specialist (AI/Healthtech): Transforming unstructured clinical notes, protocols, and medical records into structured training sets to power the Aika AI platform with an accent on high-fidelity labeling and clinical coding standards. Focus on refining machine learning models through human-in-the-loop expertise and ensuring regulatory-grade accuracy for AI outputs.
Location: Hybrid in Austin, USA
Company
is a Healthtech company revolutionizing drug development using AI to accelerate medical advancements and improve patient outcomes.
What you will do
- Lead high-fidelity labeling and annotation of diverse clinical datasets, including trial protocols and Case Report Forms (CRFs).
- Collaborate with AI and Engineering teams as a human-in-the-loop expert to refine machine learning models.
- Apply clinical coding standards such as MedDRA, WHODrug, or SNOMED CT to ensure data interoperability.
- Conduct rigorous audits of annotated data to maintain ground truth accuracy for regulatory-grade AI outputs.
- Extract key entities, including inclusion/exclusion criteria and biomarkers, from unstructured documents.
Requirements
- Bachelor’s degree in Life Sciences, Nursing, Pharmacy, or Health Informatics.
- 2+ years of experience in clinical data management (CDM) or clinical research, focusing on data cleaning or medical coding.
- Proven experience with data labeling tools or a strong understanding of NLP concepts in a medical context.
- Deep familiarity with clinical trial phases, protocol design, and FDA/EMA regulatory requirements.
- Must be based in or able to work hybrid in Austin, USA.
Culture & Benefits
- Hybrid work model offering flexibility between professional and personal life.
- Comprehensive private health coverage.
- Company-sponsored gym membership and MacBook provided.
- Pet-friendly office environment with provided coffee, tea, and snacks.
- Regular company events to celebrate achievements.
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