Data Quality Analyst | NeuroDiscovery AI · Teeming.ai
NeuroDiscovery AI
NeuroDiscovery AI is a pioneering company revolutionizing neurological care and research through its AI-powered platform and one of the largest US neurological datasets with over 5 million…
Artificial IntelligenceBiomarker DiscoveryClinical TrialsLongitudinal DataMulti-OMICSNeurologyPatient RecruitmentReal World Dataneurodiscovery.ai
NeuroDiscovery AI
NeuroDiscovery AI is a pioneering company revolutionizing neurological care and research through its AI-powered platform and one of the largest US neurological datasets with over 5 million…
Artificial IntelligenceBiomarker DiscoveryClinical TrialsLongitudinal DataMulti-OMICSNeurologyPatient RecruitmentReal World Dataneurodiscovery.ai
Position Summary
The EHR Data Quality Analyst ensures the accuracy, completeness, and integrity of healthcare data extracted from Electronic Health Record (EHR) systems. This role is responsible for validating source data, verifying data mappings, and ensuring alignment between raw clinical records and analytical datasets used for research and reporting. Working closely with data engineering, analytics, and research teams, the analyst performs structured quality control (QC) checks within the EHR and downstream databases to maintain high data reliability and consistency.
Key Responsibilities
Review and validate clinical data directly within the EHR system
Confirm that extracted datasets accurately reflect source records
Perform QC checks on patient demographics, diagnoses, medications, procedures, labs, and encounters
Use SQL to conduct data validation, reconciliation, and discrepancy analysis
Requirements
Bachelor`s degree in Health Informatics, Life Sciences, Data Science, Healthcare Administration, or related field
2—4 years of experience working with EHR data or clinical data validation
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Validate mappings between EHR source fields and analytical databases
Identify missing data, inconsistencies, and transformation errors
Support exploratory data validation using basic Python (e.g., pandas-based checks)
Collaborate with data engineering teams to resolve mapping or ETL issues
Document QC findings, root causes, and resolution steps
Contribute to development of data validation frameworks, SOPs, and best practices
Ensure adherence to data governance and compliance standards
Strong understanding of EHR data structures (diagnoses, medications, procedures, labs, encounters)
Solid SQL proficiency for validation and reconciliation
Basic working knowledge of Python for data exploration and QC
Experience performing structured data quality checks
Strong attention to detail and problem-solving ability
Clear documentation and communication skills
Familiarity with ICD, CPT, HCPCS, NDC, or other healthcare coding systems
Experience in RWE, clinical research, or healthcare analytics environments
Understanding of ETL processes and data transformation logic
Ability to work cross-functionally with technical and clinical teams