Python AI Engineer – Synthetic Test Data (Life Sciences / GxP)

Python AI Engineer – Synthetic Test Data (Life Sciences / GxP)

About the Role

Python AI Engineer

Key Responsibilities

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.

AI/ML in Data Generation

• Apply generative AI (LLMs, GANs, diffusion models) to model realistic clinical scenarios.
• Create synthetic patient journeys across EDC, eCOA, CTMS, LIMS, and PV systems.
• Use AI-driven automation to simulate rare or complex cases for stress and edge-case testing.

Compliance & Data Governance

• Ensure adherence to GxP, 21 CFR Part 11, HIPAA, and GDPR standards.
• Implement anonymization/obfuscation while maintaining clinical and statistical fidelity.
• Produce validation protocols and documentation for audit and inspection readiness.

Collaboration & Integration

• Work 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.
• Partner with SMEs to validate AI-generated datasets for regulatory acceptability.

Required Skills & Qualifications

• Strong Python expertise (pandas, NumPy, scikit-learn, PyTorch/TensorFlow, LangChain, etc.).
• Hands-on experience generating synthetic clinical data or applying CDISC standards (SDTM, ADaM, Define-XML).
• Familiarity with clinical data management systems (Medidata Rave, Veeva CDB, Oracle InForm, etc.).
• Knowledge of GxP/21 CFR Part 11 and regulated data handling.
• Experience with test automation frameworks (PyTest, Robot Framework) and validation tools (Pinnacle21).

Preferred Qualifications

• Background supporting clinical trial systems (EDC, CTMS, PV, LIMS, eCOA).
• Understanding of statistical workflows (SAS, R) and integration into test pipelines.
• Cloud experience (AWS, Azure, GCP) for secure AI/ML deployment.
• Experience with inspection readiness and validation documentation in pharma.