Clinical Data QA Reviewer
Alignerr
Job Overview
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Job Description
About the Clinical Data QA Reviewer Role at Alignerr
Alignerr is seeking a dedicated Clinical Data QA Reviewer to join our remote-first team. This role focuses on the meticulous quality assurance of clinical text and structured Electronic Health Record (EHR) data, with a localization to Chicago. The ideal candidate will possess careful judgment, ensure consistent annotation quality, and demonstrate exceptional attention to detail when working with complex datasets.
As a Clinical Data QA Reviewer, you will play a crucial part in maintaining the integrity and accuracy of our clinical data, which is vital for our operations. Your responsibilities will involve thorough review processes to identify discrepancies and ensure compliance with established standards.
Key responsibilities
- Perform meticulous quality assurance on clinical text data.
- Review and validate structured EHR data for accuracy and completeness.
- Apply careful judgment in data annotation processes.
- Maintain consistent, high-quality data annotation standards.
- Identify and report data inconsistencies and errors.
- Contribute to the improvement of data quality protocols.
Key skills/competency
- Clinical Data Review
- Quality Assurance
- EHR Data
- Data Annotation
- Attention to Detail
- Data Validation
- Clinical Text Analysis
- Healthcare Data
- Data Integrity
- Quality Control
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 experience in clinical data review, QA, and EHR systems for Alignerr.
- Showcase attention to detail: Prepare specific examples demonstrating meticulous data handling and quality assurance processes.
- Understand remote collaboration: Be ready to discuss your experience and strategies for effective remote teamwork and communication.
- Prepare for data-focused questions: Anticipate questions about clinical data standards, annotation, and problem-solving with complex datasets.
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