Statistical Programmer (Data Anonymization)

Purpose:

This function supports the protection of patient privacy when clinical trial data is shared externally.

Typical use cases:

• Regulatory submissions
• Data sharing with researchers
• Health authority requests
• External collaborations
• Clinical trial transparency initiatives

Typical responsibilities:

• Review datasets for personally identifiable information (PII).
• Apply anonymization methodologies.
• Remove or mask sensitive variables.
• Assess re-identification risk.
• Create anonymized SDTM and ADaM datasets.
• Produce anonymization reports and documentation.
• Validate anonymized outputs.

Key technical skills:

1. SAS programming.
2. SDTM and ADaM expertise.
3. Clinical trial disclosure processes.
4. Metadata and standards expertise.
5. Knowledge of data privacy regulations:

• GDPR
• HIPAA
• EMA Policy 0070

Ideal profile:

• Clinical programming teams.
• Regulatory programming.
• Clinical disclosure teams.
• Data transparency functions.
• 4+ Years of relevant experience

Success factors:

• Meticulous attention to detail
• Strong compliance mindset
• Understanding of patient privacy regulations
• Process-oriented approach
• Ability to work within heavily governed environments