AI Synthetic Data Engineer

1 week ago

Kottayam, Kerala, India beBeeData Full time ₹ 20,00,000 - ₹ 25,00,000

Job Title: Synthetic Data Generation Specialist

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Description:

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We are seeking a highly skilled Synthetic Data Generation Specialist to join our team. As a key member of our data generation initiative, you will design and implement intelligent frameworks for synthetic test data generation in support of clinical trials, regulatory submissions, and GxP-compliant systems.

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Responsibilities:

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1. Build robust Python frameworks to generate high-quality synthetic clinical and operational datasets.

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2. Produce CDISC-compliant datasets (SDTM, ADaM) for data transformation, analysis, and reporting workflows.

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3. Simulate domain-specific datasets (adverse events, labs, demographics, drug exposure) with variability and referential integrity.

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4. Develop connectors for Define-XML, Pinnacle21 validation, and statistical programming workflows.

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Ai/ml in Data Generation:

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1. Apply generative Ai (LLMs, GANs, diffusion models) to model realistic clinical scenarios.

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2. Create synthetic patient journeys across EDC, eCOA, CTMS, LIMS, and PV systems.

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3. Use Ai-driven automation to simulate rare or complex cases for stress and edge-case testing.

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Compliance & Data Governance:

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1. Ensure adherence to GxP, 21 CFR Part 11, HIPAA, and GDPR standards.

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2. Implement anonymization/obfuscation while maintaining clinical and statistical fidelity.

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3. Produce validation protocols and documentation for audit and inspection readiness.

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Requirements:

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1. Strong Python expertise (pandas, NumPy, scikit-learn, PyTorch/TensorFlow).

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2. Hands-on experience generating synthetic clinical data or applying CDISC standards (SDTM, ADaM, Define-XML).

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3. Familiarity with clinical data management systems (Medidata Rave, Veeva CDB, Oracle InForm).

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4. Knowledge of GxP/21 CFR Part 11 and regulated data handling.

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5. Experience with test automation frameworks (PyTest, Robot Framework) and validation tools (Pinnacle21).

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Preferred Qualifications:

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1. Background supporting clinical trial systems (EDC, CTMS, PV, LIMS, eCOA).

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2. Understanding of statistical workflows (SAS, R) and integration into test pipelines.

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3. Cloud experience (AWS, Azure, GCP) for secure Ai/ml deployment.

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4. Experience with inspection readiness and validation documentation in pharma.

"]},

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