Key Responsibilities
🔹 1. Core Generative Engine Development
•    Architect and implement advanced Generative Adversarial Networks (GANs) for structured, tabular, time-series, and multimodal data.
•    Develop Variational Autoencoders (VAEs) and latent variable models for representation learning and synthetic population generation.
•    Integrate Bayesian methods (Bayesian Networks, Bayesian hierarchical models, Bayesian deep learning) to incorporate uncertainty and domain priors.
•    Design hybrid generative frameworks combining deep learning and probabilistic graphical models.
________________________________________
🔹 2. Synthetic Population & Simulation Modeling
•    Build statistically consistent synthetic populations that preserve:
o    Marginal distributions
o    Joint relationships
o    Correlation structures
o    Causal dependencies (where applicable)
•    Implement scenario simulation and intervention modeling using probabilistic inference.
•    Quantify model fidelity, privacy risk (re-identification risk), and bias.
________________________________________
🔹 3. Platform & API Enablement
•    Expose generative capabilities via RESTful APIs.
•    Design modular architecture to allow regional POD-based extensions.
•    Build reusable model components to support evolving domain requirements.
•    Ensure scalability, reproducibility, and version control of generative models.
________________________________________
🔹 4. Privacy, Compliance & Governance
•    Implement differential privacy techniques where required.
•    Ensure synthetic data meets regulatory constraints.
•    Develop validation metrics for fidelity, utility, and privacy preservation.
________________________________________
🔹 5. Cross-Functional Leadership
Collaborate with product teams, domain SMEs, and business teams.
Translate domain requirements into probabilistic modelling strategies.
Mentor junior data scientists and establish modelling best practices.
Contribute to platform roadmap and long-term AI strategy.