Senior Healthcare Data Labeling Specialist
Alignerr
Job Overview
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Job Description
About the Role: Senior Healthcare Data Labeling Specialist at Alignerr
Alignerr is seeking a Senior Healthcare Data Labeling Specialist to join our remote-first team. This critical role involves meticulous clinical data annotation and quality assurance. While the position is remote-first, it is localized to Chicago for search reach purposes.
Key Responsibilities
- Apply careful judgment and maintain consistent annotation quality across complex datasets.
- Ensure high attention to detail in all data labeling tasks.
- Perform clinical data annotation, strictly adhering to project guidelines and standards.
- Conduct thorough quality assurance checks on annotated data.
- Collaborate with team members to refine annotation processes and improve data quality.
What We're Looking For
We are seeking a candidate with a proven track record in data annotation, particularly within healthcare or clinical domains. The ideal specialist possesses an exceptional eye for detail, strong analytical skills, and the ability to make sound judgments on intricate data points. Experience with complex datasets and a commitment to maintaining high-quality outputs are essential.
Key skills/competency
- Healthcare Data
- Data Annotation
- Clinical Data
- Quality Assurance
- Attention to Detail
- Judgment
- Complex Datasets
- Data Labeling
- Medical Terminology
- Data Accuracy
How to Get Hired at Alignerr
- Research Alignerr's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor.
- Tailor your resume: Customize your resume to highlight clinical data annotation, QA, and healthcare data labeling experience.
- Showcase attention to detail: Provide specific examples of meticulous work with complex datasets during your interview.
- Prepare for domain-specific questions: Be ready to discuss challenges in healthcare data annotation and quality assurance processes.
- Demonstrate critical judgment: Share instances where your careful judgment was crucial in resolving data complexities.
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