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Freelance Clinical Data Manager (m/f/d)
On behalf of a renowned client in the life sciences sector, we are seeking freelance support in the field of Clinical Data Management. The main responsibility is to coordinate all data management activities in clinical trials.
- Typical areas of responsibility in this role:**
- Creation and review of the Data Management Plan (DMP) and the Data Validation Plan (DVP).
- Design and implementation of electronic case report forms (eCRFs) in EDC systems (Electronic Data Capture).
- Coordination of data collection, validation, and cleaning throughout the study lifecycle.
- Development and execution of query processes to resolve data inconsistencies with investigational sites.
- Medical coding of adverse events (AEs) and medications (e.g., with MedDRA, WHODrug).
- Conducting SAE reconciliation (alignment of serious adverse events).
- Preparation and execution of the database lock after data collection is complete.
- Preparation of status reports and communication with the study team (project management, monitoring, biostatistics).
- Relevant technologies, tools, or methods:**
- EDC systems: Medidata Rave, Oracle Clinical, Viedoc, Zelta, OpenClinica.
- Programming/analysis tools: SAS, R, Python, SQL.
- Standards: CDISC (SDTM, ADaM), GCP (Good Clinical Practice), 21 CFR Part 11.
- Collaboration tools: MS Teams, Jira, Confluence.
- Data visualization: Tableau, Power BI.
- Typical KPIs or success metrics:**
- Data quality: Query rate (number of queries per 1,000 data points) < X; data entry error rate < 1%.
- Timeliness: Time from Last Patient Last Visit (LPLV) to database lock (target: 4–6 weeks).
- Efficiency: Average time to query resolution (target: < 10 business days for critical queries).
- Compliance: 100% adherence to milestones and processes defined in the DMP.
- Protocol adherence: Rate of protocol deviations related to data management < 5%.
- Key challenges, risks, or specifics:**
- Managing increasing volumes and sources of data (e.g., wearables, ePROs).
- Ensuring data integrity and security (cybersecurity, data protection/GDPR).
- Complexity due to mid-study changes (protocol amendments) and their impact on the database.
- Ensuring consistent data quality across globally distributed investigational sites.
- Shortage of qualified specialists and high demands for onboarding study-specific systems.
- Concrete deliverables:**
- Finalized and approved Data Management Plan (DMP).
- Live and validated study database (EDC system).
- Regular data cleaning reports and status updates.
- Clean, quality-assured database prepared for statistical analysis.
- Final locked study database with complete audit trail.
- Archived project documentation after study completion.
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- Standards: HL7, FHIR.
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- Tools: MS Project, Jira, Confluence.
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- **Deliverables:**
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- Review and approval of GMP-relevant documents such as SOPs, validation reports, deviations and change control requests.
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- Typical KPIs or success metrics:**
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- Right-First-Time Rate: Percentage of batches released without deviations or rework (Target: > 95%).
- CAPA Effectiveness: Reduction in repeat deviation rate after CAPA implementation (Target: > 80%).
- Audit Performance: Number of critical/major findings in internal and external audits (Target: 0).
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- High complexity and variability due to development of new molecules and processes in the clinical phase.
- Strict compliance with ever-evolving GMP regulations (e.g., EU Annex 1 for sterile products).
- Ensuring product quality and patient safety under high time pressure (Time-to-Market).
- Managing interfaces between development, manufacturing, QC and regulatory affairs.
- Risk of supply chain disruptions for critical raw materials or services.
- Ensuring data integrity across all quality-related systems.
- Deliverables:**
- Released batches of investigational products.
- Approved master batch records and SOPs.
- Completed and evaluated deviation and change reports.
- Audit and inspection reports with tracked action plans.
- Valid quality agreements with all relevant partners.
- Regular quality reports for management.
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⸻
Service description
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- Milestone 1: completed as-is analysis and process documentation.
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Frontend developer to HR platform with Angular experience
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Freelance Clinical Data Manager (m/f/d)
Industries
Biotechnology
Pharmaceutical
Areas
Quality Assurance (QA)
Research and Development (R&D)
Project info
- Period07.07.2025 - 07.10.2025
- Capacityfrom 90%
- Daily rate620 - 830€
- LocationMunich, Germany
- Language
- German(Native)
- German
- Remotefrom 80%
Description
On behalf of a renowned client in the life sciences sector, we are seeking freelance support in the field of Clinical Data Management. The main responsibility is to coordinate all data management activities in clinical trials.
Typical areas of responsibility in this role:**
Creation and review of the Data Management Plan (DMP) and the Data Validation Plan (DVP).
Design and implementation of electronic case report forms (eCRFs) in EDC systems (Electronic Data Capture).
Coordination of data collection, validation, and cleaning throughout the study lifecycle.
Development and execution of query processes to resolve data inconsistencies with investigational sites.
Medical coding of adverse events (AEs) and medications (e.g., with MedDRA, WHODrug).
Conducting SAE reconciliation (alignment of serious adverse events).
Preparation and execution of the database lock after data collection is complete.
Preparation of status reports and communication with the study team (project management, monitoring, biostatistics).
Relevant technologies, tools, or methods:**
EDC systems: Medidata Rave, Oracle Clinical, Viedoc, Zelta, OpenClinica.
Programming/analysis tools: SAS, R, Python, SQL.
Standards: CDISC (SDTM, ADaM), GCP (Good Clinical Practice), 21 CFR Part 11.
Collaboration tools: MS Teams, Jira, Confluence.
Data visualization: Tableau, Power BI.
Typical KPIs or success metrics:**
Data quality: Query rate (number of queries per 1,000 data points) < X; data entry error rate < 1%.
Timeliness: Time from Last Patient Last Visit (LPLV) to database lock (target: 4–6 weeks).
Efficiency: Average time to query resolution (target: < 10 business days for critical queries).
Compliance: 100% adherence to milestones and processes defined in the DMP.
Protocol adherence: Rate of protocol deviations related to data management < 5%.
Key challenges, risks, or specifics:**
Managing increasing volumes and sources of data (e.g., wearables, ePROs).
Ensuring data integrity and security (cybersecurity, data protection/GDPR).
Complexity due to mid-study changes (protocol amendments) and their impact on the database.
Ensuring consistent data quality across globally distributed investigational sites.
Shortage of qualified specialists and high demands for onboarding study-specific systems.
Concrete deliverables:**
Finalized and approved Data Management Plan (DMP).
Live and validated study database (EDC system).
Regular data cleaning reports and status updates.
Clean, quality-assured database prepared for statistical analysis.
Final locked study database with complete audit trail.
Archived project documentation after study completion.
Requirements
Must-haves:**
University degree in life sciences, (bio)informatics, statistics, or a comparable qualification.
Several years of hands-on experience in clinical data management in the pharmaceutical industry or at a CRO.
In-depth knowledge of regulatory requirements (ICH-GCP, 21 CFR Part 11) and industry standards (CDISC).
Experience with at least one common EDC system (e.g., Medidata Rave, Oracle Clinical).
Nice-to-haves:**
Certification as a Certified Clinical Data Manager (CCDM®) from the Society for Clinical Data Management (SCDM).
Programming skills in SAS, R, or SQL.
Experience leading data management subprojects.
Personal traits and mindset:**
Exceptional diligence and attention to detail.
Strong analytical and problem-solving skills.
Excellent communication and teamwork skills for collaboration in interdisciplinary study teams.
High degree of autonomy, organizational talent, and sense of responsibility.
Ability to work precisely under time pressure and set priorities.
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