
Data Management Specialist
6 days ago
The ideal candidate will have expertise in clinical data management, including project specification definition, database build configuration, CRF design, and data review and reconciliation.
">- Key Responsibilities:
- Define project specifications for Data Management services, including Protocol Conversion, Database Build, CRF Design and Data Review and Data Reconciliation tools.
- Understand external data collection, its integration into the clinical trial, and the management and reconciliation processes required to ensure its accuracy and relevance.
- Execute data cleaning strategies to accelerate time to achieve subject data cleanliness and ensure high-quality, timely deliverables.
- Perform holistic data review and trending analysis via reporting and analytics to proactively identify issues, risks, and develop mitigation strategies.
- Utilize artificial intelligence (AI) and machine learning (ML) for anomaly and outlier detection to enhance the efficiency and quality of trial data.
- Monitor and interpret key performance indicators (KPIs), metrics, dashboards, Clinical Trial Operational Analytics (CTOA), and reports to provide actionable recommendations to study leads/project managers.
- Perform Query Management.
- Define specifications and collaborate with technical teams on configuration of centralized data management platforms for data cleaning strategy and oversight activities.
- Prepare and maintain data management documentation (e.g., DMP, CCGs, Help Text, DVS) and update throughout the trial lifecycle.
- Review and ensure the quality control of team-developed deliverables, covering eCRFs, study documents, program/report specifications, outputs, and centralized data management platforms with analytics modules.
- Collaborate and work as a team to ensure deliverables are completed on time with high quality.
- Ensure compliance with industry quality standards, regulations, guidelines, and procedures.
- Other duties as assigned.
Requirements:
- Expertise in clinical data management, including project specification definition, database build configuration, CRF design, and data review and reconciliation.
- Understanding of external data collection, integration into the clinical trial, and management and reconciliation processes.
- Ability to execute data cleaning strategies to accelerate time to achieve subject data cleanliness.
- Skills in utilizing AI and ML for anomaly and outlier detection to enhance the efficiency and quality of trial data.
- Proficiency in monitoring and interpreting KPIs, metrics, dashboards, CTOA, and reports.
- Strong collaboration and communication skills.
- Experience working with centralized data management platforms.
Benefits:
- Opportunity to work with a dynamic team.
- Professional growth and development opportunities.
- Competitive compensation and benefits package.
How to Apply:
- Submit your resume and cover letter.
- Participate in our interview process.
- Join our team
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