AI-driven Synthetic Data Generation Specialist

2 weeks ago

Mangalore, Karnataka, India beBeeSynthetic Full time ₹ 16,01,000 - ₹ 25,47,000

Job Title: Synthetic Data Engineer

About the Opportunity

We are seeking a highly skilled engineer to design and implement intelligent frameworks for synthetic test data generation.

Key Responsibilities

Our ideal candidate will be responsible for building Python frameworks to generate synthetic clinical and operational datasets, producing CDISC-compliant datasets, simulating domain-specific datasets with variability and referential integrity, and developing connectors for Define-XML, Pinnacle21 validation, and statistical programming workflows.

  • Synthetic Test Data Automation: Build Python frameworks to generate synthetic clinical and operational datasets.
  • Produce CDISC-compliant datasets (SDTM, ADaM) for data transformation, analysis, and reporting workflows.
  • Simulate domain-specific datasets (adverse events, labs, demographics, drug exposure) with variability and referential integrity.
  • Develop connectors for Define-XML, Pinnacle21 validation, and statistical programming workflows.
Achieving Compliance & Data Governance

The successful candidate will ensure adherence to GxP, 21 CFR Part 11, HIPAA, and GDPR standards, implement anonymization/obfuscation while maintaining clinical and statistical fidelity, and produce validation protocols and documentation for audit and inspection readiness.

  • Ensure Compliance: Adhere to GxP, 21 CFR Part 11, HIPAA, and GDPR standards.
  • Data Anonymization: Implement anonymization/obfuscation while maintaining clinical and statistical fidelity.
  • Validation Protocols: Produce validation protocols and documentation for audit and inspection readiness.
Collaboration & Integration

The selected candidate will work closely with Clinical Data Management, Biostatistics, Pharmacovigilance, and Compliance teams to gather requirements, embed test data generation into CI/CD pipelines, trial simulations, and automated validation environments, and partner with SMEs to validate AI-generated datasets for regulatory acceptability.

  • Collaboration: Work closely with Clinical Data Management, Biostatistics, Pharmacovigilance, and Compliance teams.
  • Integration: Embed test data generation into CI/CD pipelines, trial simulations, and automated validation environments.
  • Validation: Partner with SMEs to validate AI-generated datasets for regulatory acceptability.

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