Research Scientist

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  Generative Modeling: Design, train, and validate generative machine learning models (GANs, VAEs, diffusion models, and LLM-based tabular synthesizers) to generate high-fidelity synthetic tabular, longitudinal, and multi-omic datasets.
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  Privacy Assurance & Risk Assessment: Implement rigorous privacy-preserving methodologies (differential privacy, membership inference attack testing, re-identification risk metrics) to guarantee synthetic datasets meet strict governance and compliance standards.
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  Utility & Fidelity Evaluation: Establish automated benchmark suites comparing distributional fidelity, feature correlations, cross-sectional/longitudinal validity, and downstream task performance between real and synthetic cohorts.

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  ETL & Data Standardization: Design, execute, and maintain scalable ETL/ELT pipelines to map complex, longitudinal datasets from multi-center registries and disparate source systems into common data models (e.g., OMOP CDM, PCORnet).
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  Ontology Mapping & Quality Control: Implement vocabulary mappings (SNOMED, RxNorm, LOINC, ICD-10) and robust data quality frameworks to handle missingness, inconsistent formatting, and cross-site measurement variation across clinical and molecular datasets.
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  Registry & Pipeline Integration: Architect automated validation workflows ensuring privacy preservation, site blinding, and reproducible data harmonization across large-scale cohort studies.

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  Multi-Omics Data Integration: Analyze, harmonize, and model high-dimensional biological data layers (e.g., genomics, transcriptomics, metabolomics, proteomics) alongside EHR and clinical registry data.
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  Algorithmic Modeling: Develop, evaluate, and deploy advanced machine learning models (gradient boosted trees, neural architectures, ensemble learning) and semi-parametric causal inference techniques on high-dimensional harmonized and synthetic data.
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  Longitudinal & Trajectory Analysis: Build statistical and ML frameworks for analyzing longitudinal trajectories, biological aging markers, and time-varying exposures.
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  Model Explainability & Transportability: Conduct rigorous cross-validation, sensitivity analyses, and transportability evaluations to ensure model fairness, robustness, and biological/clinical validity when trained on real vs. synthetic inputs.
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  Interdisciplinary Collaboration: Work closely with biostatisticians, bioinformaticians, privacy officers, software engineers, and domain experts to translate scientific hypotheses into secure, end-to-end data products.
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  Methodological Dissemination: Lead the drafting of peer-reviewed scientific publications, technical whitepapers, and method documentation regarding privacy metrics, synthetic data utility, and multi-omic modeling.
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  Pipeline Open-Sourcing & Mentorship: Contribute to reproducible codebases (R, Python, SQL) and mentor junior data engineers and graduate research assistants.
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Qualified candidates will hold a Master's degree plus 5 years of experience or a PhD and 2 years of experience in a related discipline to include at least 2 years of research and/or college level teaching in a field basic to the work to be performed. Degree must be conferred by the start date of the position.

How is pay for new employees determined at GW?