Lead Research Scientist Jobs

• 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.
• 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.
• 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.

• 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).
• 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.
• Registry & Pipeline Integration: Architect automated validation workflows ensuring privacy preservation, site blinding, and reproducible data harmonization across large-scale cohort studies.

• 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.
• 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.
• Longitudinal & Trajectory Analysis: Build statistical and ML frameworks for analyzing longitudinal trajectories, biological aging markers, and time-varying exposures.
• 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.

• 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.
• 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.
• Pipeline Open-Sourcing & Mentorship: Contribute to reproducible codebases (R, Python, SQL) and mentor junior data engineers and graduate research assistants.

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.

• Synthetic Data & Generative AI Expertise: Demonstrated experience with tabular synthetic data frameworks (e.g., CTGAN, TVAE, Synthcity, differentially private generative models) and privacy measurement frameworks (membership inference, attribute disclosure risk).
• Programming & Tooling:
  • Languages: Advanced proficiency in Python (PyTorch/TensorFlow, Pandas, Scikit-Learn, Synthcity/Diffprivlib) and/or R (synthpop, Bioconductor, caret, SuperLearner, targetML) is highly preferred.
  • Databases & Data Engineering: Strong SQL mastery; experience with relational databases (PostgreSQL, BigQuery, Snowflake) and distributed processing tools (Spark, dbt) is highly desired.
• Data Harmonization Expertise: Hands-on experience working with standardized healthcare/biobank data models (e.g., OMOP Common Data Model) and clinical terminology standards.
• Machine Learning & Statistics: Deep expertise in supervised/unsupervised learning, ensemble methods, survival analysis, and causal inference techniques is desired.
• Multi-Omics Expertise: Strongly Desirable: Hands-on experience processing, harmonizing, and analyzing high-throughput omics datasets (e.g., genomic variants, RNA-seq, untargeted metabolomics, panel proteomics) using standard bioinformatics pipelines (e.g., Bioconductor, PLINK, Nextflow/Snakemake) and integrating them with clinical EHR/registry frameworks.
• Experience evaluating the “train on synthetic, test on real” (TSTR) paradigm across diverse biomedical, multi-omic, or observational datasets.
• Experience working with federal funding frameworks (NIH, NSF, PCORI) or multi-center research coordinating centers (BRCCs).
• Familiarity with privacy-preserving technologies (federated learning, differential privacy, secure enclave research environments).
• Knowledge of containerized deployment (Docker, Kubernetes) and version-controlled workflow tools (Git).
• Potential to lead first-author publications in peer-reviewed journals or top-tier machine learning/data science/bioinformatics conferences (e.g., NeurIPS, KDD, JAMIA, Biometrics, Bioinformatics).

How is pay for new employees determined at GW?