The Senior Computational Scientist will play a central role in a large funded research project focused on identifying causal drivers and mechanistic pathways underlying resilience, aging trajectories, and functional decline.
Requirements
- PhD in Biostatistics, Statistics, Epidemiology (methods track), Computational Biology, Systems Biology, or a related quantitative field.
- Strong experience in causal inference, including DAG construction, confounding structures, selection bias, and identifiability conditions; familiarity with instrumental variables and debiased/orthogonal ML frameworks.
- Experience with longitudinal and time-series modeling, including state-space or Bayesian approaches, irregular sampling, and missing data; experience modeling circadian or physiological rhythms is highly desirable.
- Experience working with high-dimensional biological data (e.g., multi-omics, biomarker discovery) and interpretable biological modeling approaches.
- Judicious application of machine learning methods, including latent variable models, embeddings, and dimensionality reduction, with demonstrated judgment around when deep learning is appropriate.
- Proficiency in R as a primary programming language, with experience using packages such as DoubleML, dagitty, grf, KFAS, bssm, lavaan, mgcv, survival, ranger, and torch.
- Experience with reproducible analytical workflows and version control.
Benefits
- Comprehensive benefits package (medical, dental, vision, retirement)
- Visa sponsorship and immigration support, if needed
- Access to world-class analytical infrastructure, Buck core facilities, and multi-omics platforms
- Opportunity to contribute to pioneering research in aging, immunology, and space biosciences
- $5000 relocation support
