Data Quality Analyst (Translational Research)

Position Summary

The Data Quality Analyst will play a key role in evaluating scientific data submissions across the translational research continuum, ensuring accuracy, completeness, and adherence to established standards. This role requires attention to detail, familiarity with pre-clinical research concepts, and an interest in applying best practices to evolving areas of biomedical research, including influenza and infectious disease.

Key Responsibilities

• Review scientific data submissions for completeness, accuracy, and adherence to defined standards.
• Evaluate the consistency and scientific relevance of data and flag potential issues for review.
• Assess methodological details of pre-clinical and translational research submissions under the guidance of senior staff.
• Support the translation of data workflows into transparent, structured processes that can be adapted for automation and AI-assisted review.
• Collaborate with scientific staff, informatics teams, and data providers to resolve discrepancies and improve data quality.
• Assist in monitoring data quality metrics and document trends or recurring issues.
• Maintain up-to-date knowledge of emerging research methods, data standards, and automation tools to support improvements in data quality practices.
• Contribute to team documentation and process refinement efforts as part of continuous improvement initiatives.

Required Qualifications

• Bachelor’s degree in a relevant scientific or data-related discipline (e.g., biomedical sciences, bioinformatics, epidemiology, virology, immunology, or related field).
• Familiarity with pre-clinical research methods and experimental design.
• Strong attention to detail with the capacity to identify inconsistencies or gaps in structured scientific data.
• Ability to follow established data quality workflows and contribute to process documentation.
• Strong written and verbal communication skills, with the ability to summarize findings clearly.
• Collaborative mindset, with the willingness to seek guidance and work effectively in a cross-disciplinary team.

Preferred Qualifications

• Master’s degree in a relevant scientific or data-related field.
• Understanding of controlled vocabularies, ontologies, and biomedical data standards.
• Familiarity with database systems, structured data models, or data submission pipelines.
• Exposure to human-in-the-loop AI processes and automation in data review workflows.
• Experience with quality control, process improvement, or research data management.