Requirements
- You have a PhD in Bioinformatics, Biostatistics, Computational Biology or similar field with a strong scientific publication record. Minimum of 0-3 (Associate Scientist) or 3-5 (Scientist) years of relevant post-doctoral or industry experience is required.
- You have extensive experience with large scale high dimensional cytometry or single cell sequencing data and in-depth knowledge of analytical methods for analysis of cytometry and single cell sequencing and multi-omics data, including dimensionality reduction, clustering, statistical modeling, computational phenotyping, cell type annotation, meta analysis, data integration and machine learning.
- You have the ability to develop, compare and deploy novel analysis methods to advance biomedical research, with a thorough understanding of the underlying statistical frameworks.
- You have experience working with high dimensional data to support biomarker discovery and translation (e.g. single cell sequence assays, genotype or WGS data, proteomics data, etc.)
- You have demonstrated competence in languages such as R or Python for bioinformatics analyses with extensive experience in developing algorithms into functional informatics workflows (e.g. R packages)
- You enjoy using creative and novel informatic approaches and datasets to bring new insights to biological problems, and then working closely with biomarker scientists to develop those ideas experimentally.
- You have experience in translational research is at least one of the following therapeutic areas of Ophthalmology, Metabolism, Neuroscience, Immunology and Infectious Diseases (OMNI) and Oncology.
- You are able to present complex results, both verbally and in writing, to computational and non-computational audiences.
- You can work successfully in cross-functional teams to contribute to technical development of informatics workflows to support drug and biomarker development.
- Candidates with experience in immunology, multi-omics, cytometry informatics, multi-omic cytometry, bulk and single-cell RNA sequencing, gene signature deconvolution and analysis, biomarker analysis, genome sequencing, immune repertoire sequencing, general bioinformatics, and/or data algorithms including machine learning will be given preference.
- Experience with implementing supervised automated gating algorithms on cytometry data is a plus.
