
AI Engineer for Synthetic Data
4 days ago
We are seeking a highly skilled Python AI Engineer to design and implement intelligent frameworks for synthetic test data generation.
- Synthetic Test Data Automation
- Build advanced Python frameworks to generate synthetic clinical and operational datasets with accuracy and reliability.
- Produce high-quality CDISC-compliant datasets (SDTM, ADaM) for efficient data transformation, analysis, and reporting workflows.
- Simulate realistic 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 with seamless integration.
- AI/ML in Data Generation
- Apply generative AI techniques (LLMs, GANs, diffusion models) to model complex clinical scenarios with precision.
- Create sophisticated synthetic patient journeys across EDC, eCOA, CTMS, LIMS, and PV systems with ease.
- Use AI-driven automation to simulate rare or complex cases for stress and edge-case testing with confidence.
- Compliance & Data Governance
- E nsure adherence to rigorous GxP, 21 CFR Part 11, HIPAA, and GDPR standards with meticulous attention to detail.
- Implement robust anonymization/obfuscation methods while maintaining clinical and statistical fidelity.
- Produce comprehensive validation protocols and documentation for audit and inspection readiness.
- Collaboration & Integration
- Foster strong relationships with Clinical Data Management, Biostatistics, Pharmacovigilance, and Compliance teams to gather requirements effectively.
- Embed test data generation into CI/CD pipelines, trial simulations, and automated validation environments with smooth integration.
- Partner with Subject Matter Experts to validate AI-generated datasets for regulatory acceptability.
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