Intelligent Synthetic Data Engineer

2 weeks ago

Meerut, Uttar Pradesh, India beBeePython Full time US$ 1,20,000 - US$ 1,50,000

**Job Description:**

We are seeking a talented software engineer to design and implement intelligent frameworks for synthetic and automated test data generation in support of clinical trials, regulatory submissions, and GxP-compliant systems. This role will leverage AI/ML and Python automation to deliver high-quality, audit-ready datasets that reduce reliance on production data, accelerate testing cycles, and ensure compliance with FDA, EMA, and global regulatory standards.

Key responsibilities include building Python frameworks to generate synthetic clinical and operational datasets, producing CDISC-compliant datasets (SDTM, ADaM) for data transformation, analysis, and reporting workflows, 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.
  • 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.

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