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1 week ago
We are seeking a Python AI Engineer to design and implement intelligent frameworks for synthetic test data generation.
Key Responsibilities- To build Python frameworks that generate synthetic clinical and operational datasets, ensuring they adhere to CDISC standards (SDTM, ADaM).
- Produce datasets compliant with these standards, suitable for data transformation, analysis, and reporting workflows.
- Simulate domain-specific datasets with variability and referential integrity, focusing on adverse events, labs, demographics, and drug exposure.
- Develop connectors for Define-XML, Pinnacle21 validation, and statistical programming workflows.
- Apply generative Ai techniques, such as llms, gans, diffusion models, to model realistic clinical scenarios.
- Create synthetic patient journeys across edc, ecoa, ctms, lims, and pv systems, utilizing Ai-driven automation.
- Simulate rare or complex cases for stress and edge-case testing, ensuring regulatory compliance.
- Ensure adherence to GxP, 21 CFR Part 11, HIPAA, and GDPR standards, maintaining clinical and statistical fidelity.
- Implement anonymization/obfuscation methods while preserving the accuracy of generated data.
- Produce validation protocols and documentation for audit and inspection readiness.
- Strong expertise in Python, including libraries like pandas, numpy, scikit-learn, pytorch/tensorflow, and langchain.
- Familiarity with CDISC standards and clinical data management systems, including Medidata Rave and Veeva CDB.
- Knowledge of GxP/21 CFR Part 11 and regulated data handling practices.
- Experience with test automation frameworks and validation tools, such as Pinnacle21.
Candidate Profile
- Background in supporting clinical trial systems.
- Understanding of statistical workflows and integration into test pipelines.
- Cloud experience for secure Ai/ml deployment.
- Experience with inspection readiness and validation documentation in pharma.
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